InternetworkingIndonesiaJournal

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ISSN: 1942-9703IIJ ©

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Editors’ Introduction 1

The Effect of IPv6 Packet Size on the Implementation 3of CRC Extension Headerby Supriyanto, Iznan H. Hasbullah & Rahmat Budiarto

A Modified Weighted Clustering Algorithm for Stable Clustering 9using Mobility Prediction Schemeby S. Muthuramalingam, R. Viveka, B. Steffi Diana & R. Rajaram

Mengamankan Single Identity Number (SIN) Menggunakan QR Code 17dan Sidik Jariby Fridh Zurriyadi Ridwan, Hariyo Santoso & Wiseto Agung

EnsoTracker: Mouse Controller Device using Head Movement 21by Gunawan, Tri Kurniawan Wijaya, Indra Maryati & Edwin Seno Dwihapsoro

UTAUT Model for Understanding Learning Management System 27by I Gusti Nyoman Sedana & St. Wisnu Wijaya

The Indonesian Journal of ICT and Internet Development

Star Schema Design for Concept Hierarchy in Attribute Oriented Induction 33by Spits Warnars

Internetworking Indonesia JournalThe Indonesian Journal of ICT and Internet Development

ISSN: 1942-9703

Manuscript Language: The Internetworking Indonesia Journal accepts and publishes papers in Bahasa Indonesia and English.

Manuscript Submission: Manuscripts should be submitted according to the IEEE Guide for authors, and will be refereed in the standard way. •Manuscript pages should not exceed 7 pages of the IEEE 2-column format. It should be submitted as a Microsoft-Word file, •using the IIJ Template document which can be found at the www.InternetworkingIndonesia.org website.Manuscripts submitted to the IIJ must not have been previously published or committed to another publisher under a copy-•right transfer agreement, and must not be under consideration by another journal.Papers previously published at conferences can be submitted to the IIJ, but must be revised so that it has significant differences •from the conference version.Authors of accepted papers are responsible for the Camera Ready Copy formatted using the same IIJ Template format.•Authors are advised that no revisions of the manuscript can be made after acceptance by the Editor for publication. The ben-•efits of this procedure are many, with speed and accuracy being the most obvious. Please email your papers (or questions) to: • editor@InternetworkingIndonesia.org

Submission Guidelines: Please review the descriptions below and identify the submission type best suited to your paper.Research Papers• : Research papers report on results emanating from research projects, both theoretical and practical in nature.Short papers• : Short research papers provide an introduction to new developments or advances regarding on-going work.Policy Viewpoints• : Policy Viewpoints explore competing perspectives in the Indonesian policy debate that are informed by aca-demic research.Teaching Innovation• : Teaching Innovation papers explore creative uses of information technology tools and the Internet to im-prove learning and education in Indonesia.Book Reviews• : A review of a book, or other book-length document, such as a government report or foundation report.

Prof. Edy Tri Baskoro, PhD (ITB, Indonesia)Mark Baugher, MA (Cisco Systems, USA) Lakshminath Dondeti, PhD (Qualcomm, USA)Paul England, PhD (Microsoft Research, USA)Prof. Svein Knapskog, PhD (NTNU, Norway) Prof. Merlyna Lim, PhD (Arizona State University, USA)

Prof. Bambang Parmanto, PhD (University of Pittsburgh, USA) Prof. Wishnu Prasetya, PhD (Utrecht University, The Netherlands)Graeme Proudler, PhD (HP Laboratories, UK) Prof. Jennifer Seberry, PhD (University of Wollongong, Australia) Prof. Willy Susilo, PhD (University of Wollongong, Australia)Prof. David Taniar, PhD (Monash University, Australia)

Thomas Hardjono, PhD (MIT Kerberos Consortium, MIT, USA)

Budi Rahardjo, PhD (ITB, Indonesia)

Kuncoro Wastuwibowo, MSc(PT. Telkom, Indonesia)

Internetworking Indonesia Journal is a semi-annual peer-reviewed electronic journal devoted to the timely study of Information & Com-munication Technology (ICT) and Internet development in Indonesia. The journal follows the open-access philosophy and seeks high-quality manuscripts on the challenges and opportunities presented by information technology and the Internet in Indonesia.

Editorial Advisory Board

Co-Editors

Journal mailing address: Internetworking Indonesia Journal, PO Box 397110 MIT Station, Cambridge, MA 02139, USA.

Technical Editorial Board

Sulfikar Amir, PhD (NTU, Singapore)Moch Arif Bijaksana, MSc (IT Telkom, Indonesia)Teddy Surya Gunawan, PhD (IIUM, Malaysia)Dwi Handoko, PhD (BPPT, Indonesia)Mira Kartiwi, PhD (IIUM, Malaysia)Bobby Nazief, PhD (UI, Indonesia)Anto Satriyo Nugroho, PhD (BPPT, Indonesia)

Yanuar Nugroho, PhD (Manchester University, UK)Bernardi Pranggono, PhD (University of Leeds, UK)Bambang Prastowo, PhD (UGM, Indonesia)Bambang Riyanto, PhD (ITB, Indonesia)Andriyan Bayu Suksmono, PhD (ITB, Indonesia)Henri Uranus, PhD (UPH, Indonesia)Setiadi Yazid, PhD (UI, Indonesia)

Vol. 2/No. 2 (2010) INTERNETWORKING INDONESIA JOURNAL 1

ISSN: 1942-9703 / © 2010 IIJ

ELCOME to the Fall/Winter 2010 issue of the

Internetworking Indonesia Journal. We are pleased to

bring you this regular issue of the IIJ which contains a broad

range of papers, include those written by graduate students. As

described in the goals of the IIJ, among others the journal

seeks to be a publishing venue for graduate students (such as

Masters/S2 and PhD/S3 students) as well as working

academics in the broad field of ICT and Internet development.

This includes graduate students from Indonesian universities

and those studying abroad.

Consistent with another goal of the IIJ, the current issue of

the journal has papers written in Bahasa Indonesia. From the

outset the IIJ has sought to promote the culture of writing and

of excellent authorship in Indonesia. It is for this reason that

the IIJ is “bilingual” in that it accepts and publishes papers in

both English and Bahasa Indonesia. We believe that by

publishing papers in Bahasa Indonesia, we provide students

and other authors the opportunity to develop formal writing

skills for technical papers in Bahasa Indonesia.

The first paper in the current issue deals with the use of a

cyclic redundancy check code (CRC code) in the extension

header of an IPv6 packet. The paper proposes the introduction

of a new IPv6 header extension whose contents would be a

CRC code computed over the relevant fields of the packet.

One aim of this approach is to obviate the need for error

detection at the underlying Data Link layer of the intermediate

nodes. The paper reports a number of results from tests using a

simple IP network topology. Although introducing this new

extension header costs additional computation at the sender

and receiver, the authors claim that overall benefits is derived

by from the intermediate nodes (routers) not needing to

perform error detection at their Data Link layer prior to

routing/forwarding packets.

Mobile ad hoc networks (MANET) is the topic of the

second paper. More specifically, the paper deals with the issue

of cluster-head selection in a stable manner. The dynamic

nature of the members of a wireless ad hoc network poses a

number of interesting questions, including that of reliable

routing. With each node in an ad hoc network acting also as

wireless router, the nodes need to have the ability to self-

organize into a given network architecture, such as a cluster

network architecture. This in-turn necessitates the selection of

certain nodes to become cluster-heads. This paper proposes a

new approach to the selection of such cluster-heads. The

approach is based on the well-known weighted clustering

algorithm (WCA), and introduces a mobility prediction

scheme which looks not only at the mobility of a given node

The editors can be reached individually at the following email addresses.

Thomas Hardjono is at hardjono@mit.edu, Budi Rahardjo is at

rahard@paume.itb.ac.id, while Kuncoro Wastuwibowo is at

kwast@telkom.co.id.

but also at the relative mobility of its neighbors. The

prediction algorithm uses the node link expiration time as the

basis for the prediction scheme.

The third paper, written in Bahasa Indonesia, focuses on the

security issues around the planned use (by the Indonesian

government) of the 16-digit Single identity Number (SIN) for

every resident of Indonesia. The aim of the SIN is, among

others, to expedite access by residents to government services

by replacing non-digital credentials and other identity

documents. The paper looks at the possible use of a two-

dimensional bar code called the Quick Response (QR) code to

conveniently represent the SIN. This barcode would then be

easily readable (eg. at local government offices) by using

code-reader devices that are attachable to PC computers or

mobile phones. The paper also considers the use of biometric

scans to authenticate the user as the legitimate holder of a

given SIN. Finally, the paper offers some broad suggestions

regarding the implementation of such a SIN-based system,

including the creation of the SIN values and its associated

card, and the verification of the card and SIN at the point of

service.

Providing affordable computer-accessibility to sufferers of

quadriplegia is the topic of the fourth paper. For handicapped

persons in developing nations, the cost of devices and related

equipment represents a major issue. As such, this paper looks

into the possible development of affordable devices using

cheap off-the-shelf components. The particular device in this

case is a mouse controller, which can be operated by detecting

the user’s head movement.

The fifth paper, which is also written in Bahasa Indonesia,

addresses quite a different topic from the previous papers,

namely that of a learning management system. Following the

unified theory of acceptance and use of technology, the paper

reports some research results from experiments conducted at

the Sanata Dharma University using the Exelsa system. The

research found that performance expectancy, social influence

and facilitating conditions represented significant factors

influencing the behavioral intention of the students employing

the Exelsa system.

The last paper is one written by a graduate student on the

topic attribute oriented induction and its influence in the

design of databases within data mining. The paper provides an

illustration of the steps required to arrive at database tables

from concept hierarchies as the point of departure. The paper

provides a basic algorithm to convert a non-rule based concept

hierarchy into database tables.

Thomas Hardjono

Budi Rahardjo

Kuncoro Wastuwibowo

Editors’ Introduction

W

2 INTERNETWORKING INDONESIA JOURNAL Vol. 2/No. 2 (2010)

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Vol.2/No.2 (2010) INTERNETWORKING INDONESIA JOURNAL 3

ISSN: 1942-9703 / © 2010 IIJ

Abstract—The traditional TCP/IP stack requires verification

and regeneration of the CRC code in every router to detect

transmission errors. For IPv6 packet transmission over high

speed networks, this task is redundant because of the very rare

transmission error and thus introduces unnecessary latency. The

CRC Extension Header (CEH) was proposed to eliminate the

unnecessary duplication of error detection. Since IPv6 packets

are transmitted over Ethernet, the packet size ranges from 1280

up to 1500 bytes. In the near future, the size will increase with the

advance of underlying technology such as 100 Gigabit Ethernet.

This paper studies the effect of various IPv6 packet sizes with the

implementation of CEH on the processing time and network

latency. Experiments were done by transmitting various IPv6

packet sizes over high speed networks. The results showed that

higher packet sizes increases processing time and network

latency.

Index Terms—Routing, TCP/IP, IPv6, error correction.

I. INTRODUCTION

OUTERS in a computer network have the important role

in the packet forwarding process in order to ensure that

each packet reaches its correct destination. An IPv6 packet

transmitted through the Internet has to pass through many

routers along the network. A router can be defined as three of

the five layers of the TCP/IP stack, namely the Physical layer,

the Data Link layer and the Network layer. In general, a router

needs to decide to which node a packet has to be forwarded

next after the IPv6 header processing are done. The decision is

made in the Network layer of the router. However, before the

packet reaches the Network layer, it must pass through the

error detection process in the Data Link layer. The Data Link

layer always verifies the CRC code attached with the frame

received to ensure that the frame from the previous hop is free

from transmission error. Only the correct frame will be

Manuscript received October 26, 2010. This work was supported by the

Ministry of National Education of the Republic of Indonesia.

Supriyanto is with the Electrical Engineering Department, University of

Sultan Ageng Tirtayasa, Cilegon, Banten, Indonesia (e-mail: supriyanto@ ft-

untirta.ac.id).

Iznan H. Hasbullah is with the National IPv6 Centre (NAv6) Universiti

Sains Malaysia, Penang, Malaysia. (e-mail: iznan@nav6.org).

Rahmat Budiarto is with the School of Computer Sciences, Universiti

Sains Malaysia, Penang, Malaysia (e-mail: rahmat@cs.usm.my).

delivered up to the Network layer for forwarding and hop limit

updating.

The Network layer within a router on an IPv6 network does

a simpler task compared to the network layer of a IPv4 router.

In an IPv6 router there are no error detection processing

(header checksum checking) and fragmentation. In addition,

the size of an IPv6 header is fixed. This means that IPv6

packets are processed faster on router. However, the Data Link

layer of the router not only needs to perform CRC calculation

for the received frame but also for each frame that needs to be

forwarded to the next hop. On a large network that with

numerous routers this double CRC calculation will be repeated

in every router, as shown in Figure 1.

Figure 1: CRC Code Generation and Verification

Figure 1 shows a small network with three routers as

intermediate nodes. In this network, there will be eight CRC

calculations: four CRC code generations and four CRC code

verifications. For IPv6 packet transmission over high speed

networks which has an extremely small probability of

transmission error, those repeated tasks are unnecessary. This

is more so in the case of a reliable medium such as fiber optic

which is not influenced by either the magnetic field or electric

field. Errors within this kind of medium will be infinitesimal

that practically we can say it has a near zero probability of

occurring.

The IPv6 protocol has been designed with features that

provide better performance than the former IPv4 protocol,

The Effect of IPv6 Packet Size on

Implementation of CRC Extension Header

Supriyanto

Electrical Engineering Department

University of Sultan Ageng

Tirtayasa (UNTIRTA) Indonesia

Iznan H. Hasbullah

National Advanced IPv6 Centre Universiti Sains Malaysia

(USM) Malaysia

Rahmat Budiarto

School of Computer Sciences Universiti Sains Malaysia

(USM) Malaysia

R

4 INTERNETWORKING INDONESIA JOURNAL SUPRIYANTO ET AL.

ISSN: 1942-9703 / © 2010 IIJ

including the extensibility of IPv6 extension header. IPv6

allows the definition of a new extension header in the future

without impacting the existing infrastructure. In this paper we

propose a CRC Extension Header (CEH) as a new IPv6

extension to perform error control within the Network layer

and eliminating error control from Data Link layer [1]. In the

near future, the IPv6 packet size will be increased [2].

Implementation of a CEH for a larger packet size is still

debatable. This paper attempts to understand the effect of

varying IPv6 packet sizes on the implementation the IPv6

packet transmission over high speed networks.

II. OVERVIEW OF CRC EXTENSION HEADER

The CEH is a new extension header that is proposed to

reduce the bottleneck in IPv6 packet transmission over high

speed networks by eliminating the CRC verification and

regeneration in each and every router, as shown in Figure 1.

This idea is entirely different from the existing method that

conducts error detection and correction in the Data Link layer

of a router. This new mechanism does not require the Data

Link layer to verify and regenerate the CRC code for error

detection. However, the concept of CEH does not eliminate

overall CRC code generation and verification during IPv6

packet transmission. Generation of CRC code is done in the

sender’s machine while the verification of CRC code will be

done at Network layer of the destination node by processing

the CEH inside the received IPv6 packet. Routers are only

required to process IPv6 packet at their Network layer in the

usual manner, which consists simply of a forwarding decision

and hop limit updating.

There are three major rationales of the idea of the CEH:

Firstly, we would like to optimally utilize one of the

advantages of IPv6 features, specifically the IPv6 extension

header. IPv6 offers the opportunity to develop new extension

headers for IPv6 packet transmission improvement in the

future. Furthermore, it is not limited to 40 bytes only. In

addition, applying a new extension header within an existing

network will not disturb current IPv6 network operations.

Secondly, within new high speed networks medium — such as

fiber optic — the probability of transmission errors occurring

is very small. This allows the error detection function in

higher layer to be bypassed. Thus, the duplication of CRC

calculation could be eliminated from the Data Link layer.

Thirdly, the high transfer rate of current underlying

technology, especially gigabit Ethernet, allows the

transmission rates of more than 100 Gbps. Eliminating the

error detection process in the intermediate nodes will decrease

round trip delay from the sender to receiver. Hence, low

latency of IPv6 packet transmission over high speed networks

could be achieved.

Since all the existing extension headers already have

specific functions, designing a CEH cannot make use of any

existing extension header. Therefore we need to define a new

one. The aim is to still utilize the advantages of the current

error control but at the same time avoid its weaknesses. The

approach uses the widely deployed CRC-32 error detection

mechanism to detect transmission errors. To obtain an optimal

result, we select an appropriate generator polynomial to be

implemented in the CEH [3]. In terms of error correction, the

CEH applies retransmission procedure to overcome erroneous

packets, such as Automatic Repeat reQuest (ARQ). Details of

the CEH format and mechanism design will be presented in

the next section.

A. Format of IPv6 with CEH

Currently, there is no standard format for IPv6 extension

headers. The existing IPv6 extension headers have different

formats compared to one another. Thus, the format of the CEH

follows the draft of the generic IPv6 extension header (GIEH)

proposed in [4]. The CEH is placed after the destination

address field of the IPv6 header, as shown in Figure 2. It has

three main fields: the Next Header field, the Reserved field

and CRC-32 code field. The next header is an 8-bit indicator

to point to other extension headers following the CEH or

upper layer data. As a new extension header, the CEH has not

obtained specific number from Internet Assigned Numbers

Authority (IANA). The Reserved field is allocated for future

use instead of specific extension header type. The CRC-32

code field is the important field for the CEH. It contains the

CRC code generated by the sender machine. This code is used

only by the destination node for verification.

Figure 2: IPv6 Packet with CRC Extension Header

B. CEH Generation

The CRC-32 code field is the most important field of the

CEH. CEH generation is determined by CRC-32 code

generation. The code is generated from entire IPv6 packet

excluding hop limit field. The CRC-32 code generation

requires a generator polynomial [5]. The generator polynomial

used in the CEH was selected from three candidates, namely

the generator polynomial standardized in IEEE802.3 [6], the

generator polynomial proposed by Guy Castagnoli [7] and the

generator polynomial proposed by Philip Koopman [8]. The

test conducted showed that Castagnoli’s CRC-32C is the most

suitable generator polynomial implement for the CEH [3].

The generation process of the CRC-32 code follows the

usual algorithm used in the Data Link layer, which is already

adapted for the Network layer as shown in Figure 3 [3]. The

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sender generates a CRC-32 code and then inserts the code into

the CRC-32 code field of the CEH. When the CEH is created

in an IPv6 packet, the extension header field is first initialized

to zero except for the next header field. A complete IPv6

packet includes a main header with next header value

indicating the type of CEH, an extension header which is CEH

and a payload. The packet is now ready for transmission along

the network path until it reaches its destination.

Figure 3: CEH Generation

C. CEH Verification

The verification of CEH will be done at the final destination

by following the algorithm depicted in Figure 4 [3]. Once the

receiver receives an IPv6 packet from previous node, it will

check the next header field of the packet. When the value

indicates there is a CEH inside the packet, it extracts the CEH

and generates a new CRC-32 code from the entire IPv6 packet

(omitting the hop limit field). The CRC-32 code generated by

the receiver will be compared to the CRC-32 code found

inside the IPv6 packet, which is located within the CRC-32

code field of the CRC extension header. If both CRC-32 code

the same, then there is no transmission error in the IPv6 packet

received. The receiver then forwards the packet into higher

layer. Otherwise it will discard the packet and wait for

retransmission.

Figure 4: CEH Verification

III. EXPERIMENTS

The CEH is a new concept for error detection for the entire

IPv6 packet which is done at the Network layer. To verify the

acceptability of the new concept we performed an experiment

using the network topology depicted in Figure 5. The

experiment has two scenarios: the transmission of IPv6

packets with the CEH for error detection in Network layer and

the transmission of IPv6 packets with Frame Check Sequence

(FCS) for error detection in Data Link layer. The first

experiment simulates the new concept, while the later

simulates the existing system. The two scenarios yielded an

adequate data to justify the performance gain of the new error

detection approach compared to the current error detection.

Figure 5: Network Topology for IPv6 Packet Transmission

The topology in Figure 5 has five nodes: Device 1 as a

sender, Router 1, Router 2, Router 3 and Device 2 are the

receivers. All nodes are PC computers equipped with Core 2

Duo processors that were configured as a mini IPv6 network.

The sender is an end system installed with an IPv6 packet

generator program written in JAVA that is able to generate

both IPv6 packet with the CEH and IPv6 packet without the

CEH. Router 1, 2 and 3 represent the intermediate system of

the network whose task is to forward the packets.

In the first experiment, the routers simply forwarded the

IPv6 packets with the CEH rather than checking each and

every packet for error detection. In the second experiment the

intermediate system performs error checking for each packet

before forwarding the packet to next node in the path. The last

node is the receiver which is a PC installed with the same

program as the sender to verify all packets received.

The sender generates an IPv6 packet by adding the IPv6

main header to a TCP segment received from the upper layer.

The Network layer then generates the CRC-32 code from both

the IPv6 header and the payload using algorithm in Figure 3.

The CRC code obtained is then inserted as the CRC extension

header into the IPv6 packet. In the first scenario the Data Link

layer just adds the link layer header. There is no trailer in the

Data Link layer frame that usually performs the error detection

task. The frame without frame check sequence (FCS)

generated by the sender is then transmitted through the

experiment network.

When the IPv6 packet reaches the intermediate nodes

(namely Router 1, 2 and 3) no CRC code calculation will be

performed by the routers for error detection. The ingress frame

is just verified for its frame header by the incoming port of

router and passed to the Network layer to determine the next

6 INTERNETWORKING INDONESIA JOURNAL SUPRIYANTO ET AL.

ISSN: 1942-9703 / © 2010 IIJ

path (forwarding process). The egress frame also does not

have a Data Link layer trailer. It simply obtains the link layer

header.

The only node that will verify the IPv6 packet is the

destination node (receiver), as indicated by destination address

field of the IPv6 packet. The verification follows algorithm in

Figure 4 to check whether the packet received is free from

error. Thus, the receiver node’s task is to receive the packet in

Data Link layer, release the Data Link layer header without

computing the CRC code, and then to deliver the packet to the

Network layer for verification.

In the second experiment which is a simulation of the

current error detection mechanism, the sender generates an

IPv6 packet without the extension header. The packet is then

encapsulated by Data Link layer by adding Data Link layer

header and trailer. We refer to this frame as the IPv6 packet

with FCS. Each intermediate node will receive the packet and

process it as usual. They process the Data Link layer header

including CRC code computation to detect transmission errors

for the corresponding hop. Only the packets that are verified

as free from error will be delivered up to Network layer for

forwarding process without processing any extension header.

Before the IPv6 packet is forwarded to the next hop, the

packet will be encapsulated again in Data Link layer of the

outgoing port of the router including CRC code regeneration.

The receiver node in the second experiment captures the IPv6

packet that was addressed to it and then verifies the packet for

error detection. The correct packet will be passed to Network

layer. Otherwise, it will discard the erroneous packet and wait

for retransmission.

In order to know the effects of IPv6 packet size with the

implementation of the CEH, we measure two main metrics:

processing time and error detection capability. Processing time

is the packet processing time in each node including the CRC-

32 code computation, packet generation and packet

verification. Here it is very important to know the network

latency, which is the most important aspect of network

performance with various packet sizes. With regards to error

detection capability, it is also important to know the new error

detection capabilities to detect transmission error on varying

sizes of IPv6 packets.

IV. RESULTS AND DISCUSSION

The sender’s processing time is the time required to

generate an IPv6 packet with the CEH using the algorithm in

Figure 3. While the receiver’s processing time is the time

required to verify the IPv6 packet received and the CEH

verification using the algorithm in Figure 4. Experiments of

IPv6 packets with the CEH, using varying packet sizes from

64 bytes to 1500 bytes yielded the results as shown in Table 1.

The table shows the relationship between the sender’s

processing time and packets size of the two error detection

mechanisms on TCP/IP, namely the transmission of IPv6

packets with the CEH as a proposed mechanism and the IPv6

packet transmission with FCS (existing system) as a

comparison. This is done to learn about the impact of packet

sizes on the performance of the two error detection

mechanisms.

Table 1: Correlation between Processing Time and Packet

Size at the Sender

Size 64 128 256 512 1024 1280 1492 Average

CEH 0.689 0.718 0.772 0.883 1.039 1.067 1.133 0.900

FCS 0.682 0.686 0.778 0.788 0.834 0.876 0.909 0.793 Δ 0.007 0.132 0.006 0.105 0.205 0.191 0.224 0.107

Both the IPv6 packet with CEH and the packet with FCS

require a certain amount of time to process at the sender as

well as at the receiver. The processing time of the packet at the

sender increases with the increase in packet size. This is

because CRC code generation was done byte-per-byte of the

packet. Thus more bits in a packet means longer time to

perform processing of the IPv6 packet. Table 1 also shows that

the average of processing time for all packet sizes for the

transmission of IPv6 packet with CEH is 13.5% higher than

the transmission of IPv6 packet with FCS. This is due to the

fact that the IPv6 packet with CEH generation at the Network

layer (shown in Figure 3) is more complex compared to FCS

generation at the Data Link layer. The process to generate the

CRC code from the whole IPv6 packet requires firstly the

exclusion of the hop limit and then secondly the insertion of

the CRC code as the extension header. In contrast the FCS

generation simply divides the whole frame with the generator

polynomial and followed by appending the CRC code in the

last part of the frame.

Similar to the sender, the receiver’s processing time of the

IPv6 packet which is listed in Table 2 showed that the

processing time is affected by the packet size. The larger the

packet, the higher it’s processing time. On average the

receiver’s processing time of IPv6 packet transmission with

CEH is 16.3% higher than IPv6 packet transmission with FCS.

Note that the percentage value is higher than in the sender.

This is because the average processing time for both IPv6 with

CEH and IPv6 with FCS at the receiver is less. In other words,

the processing time of IPv6 packet at the receiver is faster than

at the sender.

Table 2: Correlation between Processing Time and Packet

Size at the Receiver

Size 64 128 256 512 1024 1280 1492 Average

CEH 0.568 0.568 0.583 0.600 0.642 0.662 0.670 0.613

FCS 0.495 0.491 0.512 0.514 0.548 0.566 0.565 0.527

Δ 0.073 0.077 0.071 0.086 0.094 0.096 0.085 0.086

The processing times of IPv6 packets with the CEH as

listed in Table 1 and Table 2 is based on the algorithm in

Figure 3 and Figure 4. Even though the process of generating

the CEH at the sender and at the receiver is similar, the sender

has to generate the IPv6 packet first before it can generate the

CEH. In contrast the receiver simply has to receive IPv6

packets without needing to generate any packets.

However, note that the transmission of IPv6 packets

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exploiting the CEH as an error detection method in the

Network layer has eliminated the need for CRC code

calculation and regeneration in every router. This resulted in

the faster processing of IPv6 packets in every router in the

network, where a router only processes the packets at the

Network layer (routing and forwarding). Hence, although

processing time at the end systems (sender and receiver) is

higher, the total network latency of IPv6 with CEH

transmission is visibly lower than IPv6 with FCS. Figure 6

shows the correlation between network latency in milliseconds

(ms) and packet sizes in bytes. In the figure, network latency

of the IPv6 packet with CEH transmission is 68 % lower than

IPv6 with FCS. The network latency as shown in Figure 6

increases when the packet size increases. Compared to FCS,

the increasing network latency of IPv6 with the CEH is lower

as shown by the two graphs in Figure 6.

Figure 6: Network Latency

Another measurement is the error detection capability. Error

detection capability of an error control mechanism is the

ability to catch transmission errors during transmission from

the sender to the receiver. Recent high speed network

technologies have made the occurrence of transmission errors

very rare. Employing more error detection tools is inefficient

and also redundant. However, we cannot eliminate the tools

entirely because if an error occurred during the

communications then data will be corrupted. Proposing to

exploit the CEH as an error detection tool is the correct way. It

can reduce the redundant error control process but at the same

time it does not ignore the error.

Error is unpredictable. Thus an error control mechanism

should be able to detect the error inside the packet transmitted

and correct it. In order to know ability of CEH to detect

transmission errors, we send erroneous IPv6 packet with the

CEH. The result illustrated that all erroneous IPv6 packets

sent are successfully detected by the receiver.

V. CONCLUSION

In this paper we have proposed a new concept of handling

transmission errors by exploiting the IPv6 extension header.

The new concept utilizes the CRC extension header (CEH) as

an error control method at the Network layer and eliminates

error control at the Data Link layer of the intermediate nodes.

Typically, the extension header is generated by the sender and

is processed by the receiver. Thus, the CEH that contains the

CRC code is also processed by the receiver.

Using the CEH as a new IPv6 extension header for error

detection at the Network layer increases the processing time of

IPv6 packets both at the sender and at the receiver. The

increase in processing time is influenced by the packet size.

The higher the packet size the longer it will take to process the

packet. However, our proposed new concept has eliminated

error detection routine at the Data Link layer.

Elimination of error detection at the Data Link layer means

eliminating CRC code computation and regeneration in each

and every router. This reduces the total network latency on

IPv6 packet transmission. The results show that overall

network latency decrease by 68% compared with normal

latency. It also reduces the Data Link layer frame size due to

the elimination of 4 bytes of frame check sequence field. The

proposed error control mechanism also shows good ability to

detect transmission error inside the transmitted packet. The

experiment showed that all IPv6 packets sent with errors are

successfully detected by the receiver.

REFERENCES

[1] Supriyanto, Raja Kumar Murugesan, Rahmat Budiarto, Sureswaran Ramadass, CRC Extension Header (CEH): A New Model to Handle

Transmission Error for IPv6 Packets over Fiber Optic Links, Proceedings of IEEE Symposium on Industrial Electronics and

Applications (ISIEA 2009), October 4-6, 2009, pp. 569 – 573.

[2] Supriyanto, Iznan H. Hasbullah, Rahmat Budiarto, Exploiting IPv6 Extension Header to Handle Transmission Error for IPv6 Packets Over

High Speed Networks, Proceedings of International Conference on Advanced Computer Science and Information System, 7 – 8 December

2009.

[3] Supriyanto, Abidah M. Taib, Rahmat Budiarto. Selecting a Cyclic

Redundancy Check (CRC) Generator Polynomial for CEH (CRC Extension Header), Proceedings of International Conference on Quality

in Research, Jakarta. 3 – 6 August 2009, ISSN 114-1284 pp. 245 – 250

[4] S. Krishnan, et. al, An uniform format for IPv6 extension headers, Internet Draft IETF, 2008. http://ietfreport.isoc.org/idref/draft-krishnan-

ipv6-exthdr/

[5] A.S. Tanenbaum, Computer Networks, Fourth Edition, Prentice Hall of India. New Delhi, 2006.

[6] ANSI/IEEE Standard for Local Area Networks, Carrier Sense Multiple Access with Collision Detection (CSMA/CD) Access Method and

Physical Layer Specifications. 1984. http://standards.ieee.org/getieee802/802.3.html.

[7] Castagnoli, G., Brauer, S. & Herrmann, M. Optimization of cyclic

redundancy-check codes with 24 and 32 parity bits. IEEE Transactions on Communications, 1993, pp. 883-892.

[8] Koopman, P. 32-bit cyclic redundancy codes for Internet applications,

Proceedings. International Conference on Dependable Systems and Networks (DSN). 2002.

http://www.ece.cmu.edu/~koopman/networks/dsn02/dsn

8 INTERNETWORKING INDONESIA JOURNAL SUPRIYANTO ET AL.

ISSN: 1942-9703 / © 2010 IIJ

Supriyanto received the B.Eng. degree in

Electrical Engineering from Brawijaya University

Malang, Indonesia in 1999, and and M.Sc in

Computer Science from Universiti Sains Malaysia

(USM) in 2010. Currently, he is a lecturer and also

department secretary in the Electrical Engineering

Department at the University of Sultan Ageng

Tirtayasa (UNTIRTA). He is a researcher in the

Computer Network Laboratory at the university.

His research interest includes computer networks

especially, IPv6, IPTV over overlay network and

wireless communication.

Iznan H. Hasbullah graduated with the B.Sc

degree in electrical engineering in 1998 from

Rensselaer Polytechnic Institute, Troy, New York.

He is currently a Research Officer with National

Advanced IPv6 Centre (NAv6) at University Sains

Malaysia, Pulau Pinang. He is the Domain Head of

HD Video Conference for IPv6 group under Next

Generation Multimedia & Telemedicine Cluster at

NAv6. Prior to joining NAv6 in 2009, he was an

R&D Consultant with MLABS System Berhad

(MLABS) seconded to JMCS Sdn. Bhd. to

spearhead a project titled High Definition Video

Conferencing for IPv6 Networks. He held the post

of Chief Technology Officer (CTO) while with

JMCS Sdn. Bhd. He was involved in two network

security audit exercise commissioned by Malaysian

Communications and Multimedia Commission

(MCMC) in the year 2003 and 2004. He was also

part of a team that brought Advance Military

Mobile Conferencing System (AMMCS) to the

Maritime Langkawi International Maritime &

Aerospace Exhibition (LIMA'03) in 2003. His area

of expertise includes multimedia conferencing

application for computer supported cooperative

work (CSCW), software architecture, Graphical

User Interface (GUI), Component Object Modeling

(COM), Microsoft Foundation Classes (MFC) and

C++ Language.

Rahmat Budiarto received the B.Sc. degree from

Bandung Institute of Technology (ITB) in 1986,

and the M.Eng and Dr.Eng degree in Computer

Science from Nagoya Institute of Technology in

1995 and 1998 respectively. Currently, he is an

associate professor at the School of Computer

Sciences at the Universiti Sains Malaysia (USM) as

well as the advisor of Indonesia IPv6 Task Force.

His research interest includes IPv6, network

security and Intelligent Systems. He was chairman

of APAN security working group.

Vol.2/No.2 (2010) INTERNETWORKING INDONESIA JOURNAL 9

ISSN: 1942-9703 / © 2009 IIJ

Abstract—This paper proposes an idea for selecting stable

cluster heads using a modified Weighted Clustering Algorithm

and combining it with Link Expiration Time calculation. The

mobile ad hoc network consists of nodes that move freely and

communicate with each other. One way to support efficient

communication between nodes is to partition ad hoc networks

into clusters. Many clustering schemes have been proposed to

form clusters. The WCA has improved performance compared

with other previous clustering algorithms. However, the high

mobility of nodes will lead to high frequency of re-affiliation

which will increase the network overhead. To solve this problem,

we propose a time-based WCA which can enhance the stability of

cluster formation followed by stable cluster head selection. Then

duration of nodes that are alive is considered. Meanwhile for

forthcoming nodes the duration of link between them and the

Cluster head is calculated. This is the Link Expiration time and it

is calculated based on the three factors, namely position, speed

and direction of nodes. If the calculated link expiration time is

greater than the threshold value it is allowed to join the

respective cluster. This is done to form stable clusters and to

reduce the re-affiliation frequency.

Index Terms—Adhoc Networks, Stable clustering, Weighted

clustering algorithm, re affiliation, mobility prediction, link

expiration time.

I. INTRODUCTION

N recent years, the development in wireless communication,

and the widespread use of mobile and handheld devices, has

resulted in the increasing popularity of mobile ad hoc

networks (MANET). A mobile ad hoc network is a collection

of wireless mobile nodes that dynamically form a network

without the need for any pre-existing network infrastructure.

Every node in the network is serving as a router, which means

that every node is able to forward data to other nodes in the

same transmission range. When wireless nodes are in an area

that is not covered by any existing infrastructure, one of the

possible solutions to achieve the ubiquitous computing is to

enable wireless nodes to operate in the ad hoc mode and self-

organize themselves into cluster based network architecture.

S. Muthuramalingam can be reached at smuthoo@gmail.com. R. Viveka

can be reached at shekinviveka@gmail.com. B. Steffi Diana can be reached at

dianasaturcall@gmail.com. R. Rajaram can be reached at

rrit@tce.edurrit@tce.edu.

Ad-hoc networks do not use specialized routers for path

discovery and traffic routing develops the wireless backbone

architecture. This means that certain nodes must be selected

to form the backbone. One of the general approaches to build

up a cluster based network architecture is to design an

algorithm to self-organize themselves into cluster based

network architecture. This is achieved by partitioning ad hoc

networks into clusters. Certain nodes, known as cluster-

heads, would be responsible for the formation of cluster and

maintenance of the topology of the network, and also for the

resource allocation to all the nodes belonging to their clusters.

The clustering algorithm is usually performed in two phases:

clustering formation and clustering maintenance [3]. In the

clustering formation phase, the election of a cluster-head

among the nodes in the network is conducted. The functions of

cluster heads are the coordination of the clustering process and

relaying routers in data packet delivery. After electing cluster-

heads, some of nodes move out from the current cluster and

are attached to another cluster. Other cluster-heads are

downed. This leads to the second phase, namely, clustering

maintenance. In light of this, this paper aims to avoid

excessive computation in the cluster maintenance, and current

cluster structure should be preserved as much as possible.

In this paper, we propose an enhancement on a weighted

clustering algorithm [1, 5] (WCA) by maintaining stable

clusters. In WCA, a node is selected to be the cluster-head

when it has the minimum weighted sum of four indices: the

number of potential members, the sum of the distances to

other nodes is its radio distance, the node‟s average moving

speed and the battery life of the node. However, only the

individual nodes‟ mobility is measured in WCA, whereas the

relative mobility of a node and its neighbors is more important

to the cluster stability. The ignorance of the relative mobility

will lead to the situation that the high mobility of nodes causes

a high frequency of re-affiliations, which will increase the

network overhead. To solve this problem, we propose an

enhanced weight-based clustering algorithm using mobility

prediction scheme (MWCAMP). This can enhance the

stability of the network by taking the relative mobility of a

node and its neighbors into consideration for the clustering

formation [1, 2, 5] and maintenance, by predicting the

mobility of a node using the link expiration time [12].

The remainder of this paper is organized as follows. In

Section 2, we review the several relevant clustering algorithms

proposed previously and their limitations. Section 3 presents

A Modified Weighted Clustering Algorithm for

Stable Clustering using Mobility Prediction Scheme

S. Muthuramalingam, R. Viveka, B. Steffi Diana and R. Rajaram Department of Information Technology, Thiagarajar College of Engineering,

Madurai, India

I

10 INTERNETWORKING INDONESIA JOURNAL MUTHURAMALINGAM ET AL.

the proposed algorithm for ad hoc networks. Section 4

discusses the proposed algorithm. Section 5 discusses the

simulation results and analysis. Finally, Section 6 concludes

this paper.

II. RELATED WORKS AND CURRENT MOTIVATION

Recently a number of clustering algorithms have been

proposed to choose cluster-heads based on the speed and

direction, mobility, energy, position, and the number of

neighbors of a given node. These efforts present advantages

but also have some drawbacks, such as the high computational

overhead for both clustering algorithm execution and update

operations.

The Highest Degree Algorithm is also known as

connectivity-based algorithm [3]. This algorithm is based on

the degree of nodes assumed to be the number of neighbors of

a given node. Whenever the election procedure is needed,

nodes broadcast their Identifier (ID) which is assumed to be

unique in the same network. According to the number of

received IDs every node computes its degree and the one

having the maximum degree (no of adjacent nodes) becomes

cluster-head (CH). Major drawbacks of this algorithm include

the situation where the degree of a node changes very

frequently, and thus the CHs are not likely to play their role as

cluster-heads for very long. In addition, as the number of

ordinary nodes in a cluster is increased, the throughput drops

and system performance degrades. All these drawbacks occur

because this approach does not have any restrictions on the

upper bound on the number of nodes in a cluster.

The Lowest-Identifier (LID) is also known as identifier-

based clustering algorithm [4]. This approach chooses the

node with the lowest ID as a cluster-head. The system

performance is better than Highest-Degree in terms of

throughput. Major drawbacks of this algorithm are its bias

towards nodes with smaller IDs which may lead to the battery

drainage of certain nodes, and it does not attempt to balance

the load uniformly across all the nodes.

The Distributed Clustering Algorithm (DCA) [6, 7] and

Distributed Mobility Adaptive Clustering Algorithm (DMAC).

In this approach, each node is assigned weights (a real number

above zero) based on its suitability of being a cluster-head. A

node is chosen to be a cluster-head if its weight is higher than

any of its neighbor‟s weight; otherwise, it joins a neighboring

cluster-head. The smaller weighted node ID is chosen in case

of a tie. The DCA makes an assumption that the network

topology does not change during the execution of the

algorithm. To verify the performance of the system, the nodes

were assigned weights which varied linearly with their speeds

but with negative slope. Results proved that the number of

updates required is smaller than the Highest-Degree and

Lowest-ID heuristics. Since node weights were varied in each

simulation cycle, computing the cluster-heads becomes very

expensive and there are no optimizations on the system

parameters such as throughput and power control.

The Weighted Clustering Algorithm (WCA) [5] obtains

1-hop clusters with one cluster-head. The election of the

cluster-head is based on the weight of each node. It takes four

factors into consideration and makes the selection of cluster-

head and maintenance of cluster more reasonable. The four

factors are node degree (number of neighbors), distance

summation to all its neighboring nodes, mobility and

remaining battery power. Although WCA has proved better

performance than all the previous algorithms, it has a

drawback in needing to know the weights of all the nodes

before starting the clustering process and in draining the CHs

rapidly. As a result, the overhead induced by WCA is very

high.

III. PROPOSED WORK

In this section, we present the proposed Enhanced

Weighted Clustering Algorithm using mobility prediction

scheme (MWCAMP). MWCAMP consists of the clustering

formation and clustering maintenance phases. Before

describing our clustering algorithm in detail, we make the

following assumptions:

I. The network topology is static during the execution of

the clustering algorithm.

II. Each mobile node joins exactly one cluster-head.

A. Phase-I: Clustering Formation

In WCA, the goal is to minimize the value of the sum of all

the cluster heads‟ weighted costs. Here a node is selected as

cluster head when it minimize a function of four criteria such

as degree (the number of direct link to its neighbors), sum of

distance between cluster head and other nodes, mobility of

nodes and battery power of the nodes. WCA has high re-

affiliation frequency [7, 8] which will increase the

communication overhead. Thus, we present an enhanced

algorithm to reduce the amount of re-affiliation frequency

corresponding to the number of nodes that is not „„proper‟‟ in

current cluster. It corresponds to nodes that detach from

current cluster and move to another cluster. In order to reduce

the re-affiliation frequency, we should decrease the number of

improper nodes. When the idea reflects on the cluster head

election, the key is to choose „„stable‟‟ nodes as cluster heads

as possible. The stability of a node is relative to its neighbors.

In WCA, the running average speed for every node till

current time T, it is computed [9, 10] as

1

2 2 21 1

1

1(( ) ( ) )

T

i t t t t

t

M X X Y YT

where (Xt, Yt) and (Xt-1, Yt-1) are the coordinates of the node i

at time t and (t -1), respectively. It is shown that is the

absolute mobility of node and cannot indicate information of

stability between node and its neighbors. Therefore we present

an improved parameter ̅ (relative mobility parameter) to

substitute (absolute mobility parameter). Here ̅ is

inversely proportional to stability factor which is measured

in sense of relative mobility.

Vol.2/No.2 (2010) INTERNETWORKING INDONESIA JOURNAL 11

ISSN: 1942-9703 / © 2009 IIJ

The stability [13] of node “i” is computed from two aspects:

I. i : The relative average speed of i and its neighbors.

The larger i is, the more unstable the node is,

comparing to its neighbors.

II. iNC : The change of node‟s neighbors. The value of

iNC can indicate the change of topology surrounding

node i.

We also use a combined function of i and iNC to

computeiS . Another improvement of our algorithm is that we

compute the average relative distances instead of the sum of

distances.

The proposed MWCAMP described as follows

1 2 3 4ii i i iw w w D w M w P

where

I. i is the degree of each node i (i.e., nodes within its

transmission range)

II. iD is the average relative distances each node i with

all its neighbors.

III. iM is the relative mobility of node i

IV. iP is the battery power of node i.

where w1, w2 , w3 and w4 are the weighting factors for the

corresponding system parameters.

Figure 1

Figure 2

B. Phase-II: Clustering Maintenance Using Mobility

prediction

The mobility of nodes coupled with the transient nature of

wireless media often results in a highly dynamic network

topology. Due to mobility some nodes will detach from the

current cluster and attach itself to some other cluster. The

process of joining a new cluster is known as re-affiliation. If

the re-affiliation fails, the whole network will recall the cluster

head election routine. One disadvantage of WCA is high re-

affiliation frequency. High frequency of re-affiliation will

increase the communication overhead. Thus, reducing the

amount of re-affiliation is necessary in ad hoc networks. To

prevent this we go for mobility prediction schemes. The

impact of mobility prediction schemes on the temporal

stability of the clusters obtained using a mobility-aware

clustering framework. We propose a simple framework for a

mobility prediction-based clustering to enhance the cluster

stability.

One way to predict the mobility of nodes is using the

Link Expiration Time [13]. The impact of mobility prediction

schemes on the stability of the clusters obtained using a

mobility-aware clustering framework. Compute the Link

Expiration Time (LET) to predict the duration of a wireless

link between two nodes in the network. The approach assumes

that the direction and speed of motion of the mobile nodes

does not change during the prediction interval.

C. Link Expiration Time (LET)

The Link Expiration Time (LET) is a simple prediction

scheme that determines the duration of a wireless link between

two mobile nodes. Dynamic clustering in ad hoc networks has

also been extensively studied in the literature. Several

distributed clustering algorithms for MANETs have been

proposed. While some schemes try to balance the energy

consumption for mobile nodes, others aim to minimize the

clustering-related maintenance costs. Combined metrics based

clustering schemes take a number of metrics into account for

cluster configuration. The Weighted Clustering Algorithm

(WCA) [5] is one such scheme, where four parameters are

considered for the cluster head election procedure, which are

representative of the degree, the sum of the distances to other

nodes in its radio distance, mobility, and battery power of the

mobile nodes. Here we propose an enhanced WCA which can

enhance the stability of the network. Such a scheme can be

tuned flexibly the parameters to suit to different scenarios. To

calculate the duration of link between two mobile nodes, we

assume that their location, speed and direction of movement

remain constant.

Here let:

Location of node i and node j at time t be given by

(xi , yi) and (xj ,yj).

Vi and V j be the speeds,

θi and θj be the directions of the nodes i and j

respectively.

12 INTERNETWORKING INDONESIA JOURNAL MUTHURAMALINGAM ET AL.

If the transmission range of the nodes is r, then the

link expiration time Dt is given by the formula given

below

2 2 2 2

2 2

( ) ( ) ( )

( )t

ab cd a c r ad bcD

a c

where

cos cosi i j ja v v

i jb x x

sin sini i j jc v v

i jd y y

The LET gives an upper bound on the estimate of the

residence time of a node in a cluster. In the proposed

clustering framework, when LET-based prediction is used, a

node is allowed to join a cluster only if the predicted LET of

the link between the node and the cluster head is greater than

the cluster‟s admission criteria Tj [13, 14]. For every node N

that detach from current cluster we check whether the node is

a Cluster Head (or ) Cluster member.

I. If it is a Cluster Head then call for cluster head election

within the particular cluster and form a new cluster.

II. If it is a Cluster member then calculate Link Expiration

Time with Cluster Head of each cluster and the node that re-

affiliate must be within transmission range of cluster head

where transmission range is fixed.

Check whether LET is greater than threshold value (Tj),

Here Tj is average of all LET, and if it is greater then the Node

is eligible to join the particular cluster which shares greater

LET.

Figure 3

Figure 4

IV. ENHANCED WEIGHTED CLUSTERING

ALGORITHM USING MOBILITY PREDICTION

Consider Node Ni in transmission range r, positions of

node Ni be ( Xi Yi ), Nj be nodes that detach from current

cluster, where 1 ≤ i ≤ n , 1 ≤ j ≤ n and 1 ≤ k ≤ n.

1: calculate degree of node Ni

i ( )ijC

where Cij =1, if nodes Ni and Nj are connected else Cij =0 .

(Where Cij be connection between node Ni and Nj, Where 1

≤ i ≤ n , 1 ≤ j ≤ n).

2: calculate relative sum of distance iD from nodes iN to its

members,

{ ( , )}

j i

i j

i

N N i

dist N ND

where 1 ≤ i ≤ n, Nj is the node the establish link with the node

Ni.

3: iM is the running average speed for every node iN till

current time T is calculated using formula

1

2 2 21 1

1

1(( ) ( ) )

T

i t t t t

t

M X X Y YT

Vol.2/No.2 (2010) INTERNETWORKING INDONESIA JOURNAL 13

ISSN: 1942-9703 / © 2009 IIJ

4: For every node iN , compute the average speed iA using

formula

1

j i

i i

N Ni

A M

Where 1 ≤ i ≤ n, Nj is the node that establish link with the

node Ni

5: compute the speed-difference i as

i i iM A

where 1 ≤ i ≤ n.

6: For every node Ni compute the neighbor change NCi at time

t.

}{}{

}{}{

21

21

tt

tt

ii

ii

i

NN

NNNC

7: Compute the stability factor S i as

1

11 ii is a NC

where a is an adjustable integer which is decided by the

stability of the node.

8: Thus we can get the improved parameter iM as

i

i

bM

S

where b is an integer decided by the speed of node. Both a and

b are constant to make Si more reasonable, avoiding to be too

large or less.

9: Calculate weight value

1 2 3 4ii i i iw w w D w M w P

where Pi is the battery power of node, and w1= 0.7, w2= 0.05,

w3= 0.05, and w4= 0.2.

10: The node with minimum weight value is elected as

Cluster Head. That is

)}(min{ iii NwCH .

11: Now cluster members are added to the cluster head. The

nodes that establish link with that cluster head are called

cluster members and they are grouped together to form a

cluster. Thus cluster is formed by

[ { | }]

{ }

where

{ }

are previously formed cluster, where 1=k=n. Repeat step 1 to

8 until every node is a member of cluster.

12: For every node N that detach from current cluster we

check whether the node is a Cluster Head (or Cluster

member).

I. If it is a Cluster Head then call for cluster head

election within the particular cluster and form a new

cluster.

II. If it is a Cluster member then calculate LET (Link

Expiration Time) with Cluster Head CHi of each

cluster Ci and Nj must be within transmission range of CHi .where transmission range is fixed.

LET i be the duration of the link between the two nodes is

given by the formula given above in phase II and III.

13: Admission Criteria:

Check whether LET is greater than threshold value (Tj).

If it is greater then the Node Nj is eligible to join the particular

cluster Cj which shares greater LET (where Tj is average of all

LET calculated). Repeat steps 10 and 11 for every node detach

from current cluster. Else END.

V. EXPERIMENTAL SETUP AND RESULTS

In this section, we present the performance of the

proposed WCA simulated by NS2.We simulate a system of N

nodes on an 1500 m × 1500 m area. The value of N is varied

between 10 and 70 and transmission range is 500m. The nodes

move randomly in all directions with a maximum speed varied

between 20m/s and 50 m/s. The power for each node is varied

between 40 to 100 units. Because the mobility of node is an

important parameter in our case, the corresponding weight is

chosen to be high. The values of the weights used in our

simulation are: w1 = 0.7, w2 = 0.05, w3 = 0.05 and w4 =

0.2. Note that the values may be adjusted according to the

system requirements.

The performance of algorithms is measured in terms of the

number of re-affiliation count and no of clusters formed.

14 INTERNETWORKING INDONESIA JOURNAL MUTHURAMALINGAM ET AL.

Figure 5: Average re-affiliation frequency vs node number

Figure 6: Comparison between WCA and our proposed work on basis of no of clusters formed.

Figure 5 depicts the average re-affiliation count versus the

total number of nodes in the network where maximum speed

is 50m/s and the transmission range is 500m. Here the nodes

are organized into clusters and cluster head is elected. In

previously designed clustering framework using weighted

clustering algorithm, re-affiliation time for a given number of

nodes are determined and plotted. In our enhanced weighted

clustering algorithm, nodes form into clusters and elect cluster

head and the node which detach from current cluster are

joined by predicting their mobility using link expiration time.

Here future prediction (mobility) is done on basis of direction

and speed of nodes and so re-affiliation frequency is reduced.

As the number of nodes increased, the increasing rate of re-

affiliation slowed down. According to the result, MWCAMP

gives less re-affiliation than WCA especially for the high node

density. Thus we form a stable cluster with MWCAMP.

Here the number of clusters formed for a given number of

nodes is plotted. The number clusters formed should be

reduced. Here no of clusters formed are comparatively

reduced.

Comparison between WCA and our proposed work on

basis of throughput and simulation time is given in figure 7

and 8. Figure 7 represents the throughput of forwarding

message in WCA. Figure 8 represents the throughput of

forwarding message in our proposed work MWCAMP.

Figure 7: This graph represent WCA output. The Transmission

time is from 1sec to 2 sec. Here throughput of forwarding message is decreasing and it is not stable.

Vol.2/No.2 (2010) INTERNETWORKING INDONESIA JOURNAL 15

ISSN: 1942-9703 / © 2009 IIJ

Figure 8: This graph represent MWCAMP output. The

Transmission time is from 1sec to 2 sec. Here throughput of forwarding message is increasing and stable.

VI. CONCLUSION

In this paper we have presented an enhanced weight

based clustering algorithm using mobility prediction

(MWCAMP) that can be applied in MANETs to improve upon

their stability and to reduce re-affiliation of the nodes.

MWCAMP mainly focuses on reducing the instability caused

by high-speed moving nodes, by taking relative mobility of

node and its neighbors into consideration. Since WCA support

stable cluster head election and the disadvantage is re-

affiliation of nodes which is reduced by mobility prediction, it

results in stable clustering. The performance of the proposed

MWCAMP demonstrated that it outperforms WCA in terms of

re-affiliation count.

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(March 2009).

[10] Rui Xu, Wunsch, “Survey of clustering algorithms”. IEEE Transactions

on Neural Networks, Volume 16, Issue 3. (May 2007).

[11] Yingpei Zeng, Jiannong Cao, Shanqing Guo, Kai Yang, Li Xie. “A

Secure Weighted Clustering Algorithm in Wireless Ad Hoc Networks”.

IEEE conference on Wireless Communications and Networking

Conference. (5-8 April, 2009).

[12] (Bricard-Vieu.V, Nasser. N, and Mikou, NA. “Mobility Prediction-based

Weighted Clustering Algorithm Using Local Cluster-heads Election for

QoS in MANETs”. IEEE International Conference on Wireless and

Mobile Computing, Networking and Communications. (2006).

[13] Andronache, A, Rothkugel, S. “NLWCA Node and Link Weighted

Clustering Algorithm for Backbone-Assisted Mobile Ad Hoc

Networks”. Seventh International Conference on Networking. (13-18

April 2008).

[14] Bouk S.H, Sasase I. “Energy Efficient and Stable Weight Based

Clustering for mobile ad hoc networks”. 2nd International Conference

on Signal Processing and Communication Systems. (15-17 Dec. 2008).

S. Muthuramalingam has over 6 years of teaching

experience and is currently working as a Asst Prof

in Thiagarajar college of Engineering. He finished

his bachelor of Engineering (computer science) at

Shanmugha college of engineering, Thanjavur in

the year 1997. And he completed his M.E in

Kalasalingam University in 2002. He published two

papers in International journal on applications of

graph theory in wireless ad hoc networks and

sensor networks, and IJCEE. He has published

papers in the national and IEEE international

conferences such as IEEE AMS 2008 IEEE

Netcomm2010, NCCN‟09,VIT, NCACT,SAATRA

and NCIM 2007. He is pursuing PhD degree in the

field of Mobile communication at Anna University

Chennai, India.

16 INTERNETWORKING INDONESIA JOURNAL MUTHURAMALINGAM ET AL.

R. Rajaram completed his PhD in TCE, Madurai

Kamaraj University in the year 1979, and the

MTech degree in IIT Karagpur in the year 1971. He

has published about 100 papers in a number of

journals such as the International journal of

Computational Engineering Science, Journal of

Optics and Journal of the Institute of Engineers. He

is a life member of Institution of engineers,

Computational Society of India, India Society for

Tech Education. Currently working as the Dean

CSE/IT & Head of the Department of IT in

Thiagarajar college of engineering..

Vol.2/No.2 (2010) INTERNETWORKING INDONESIA JOURNAL 17

ISSN: 1942-9703 / © 2010 IIJ

Abstract—The Indonesian government is planning to

implement the Single Identity Number (SIN) as the primary

identification method for residents of Indonesia. With this SIN

implementation, it is hoped that a number of residential

administrative issues, such as an individual having multiple

identity cards (KTP), can be resolved. Furthermore, residents

will have the advantage of only needing one identity form and not

having to remember multiple identity information. It is hoped

that just by using a single SIN a resident can obtain various

government services more efficiently. However, on the other

hand the use of the SIN introduces the potential for security

attacks through the misuse of a SIN by people other than its

owner or assignee. As such additional security mechanisms are

needed to ensure that a SIN is used only by its proper owner or

assignee. This paper aims at describing a proposal for a

mechanism to secure the SIN, based on the combined use

fingerprints and the QR Code.

Pemerintah Indonesia telah merencanakan bahwa pada tahun

2012 akan diimplementasikan Single Identity Number (SIN)

sebagai identitas tunggal penduduk Indonesia. Dengan adanya

SIN ini maka diharapkan permasalahan-permasalahan

administrasi kependudukan seperti misalnya satu orang

memiliki lebih dari satu buah KTP dapat diatasi. Selain itu bagi

penduduk juga akan diperoleh kemudahan lainnya yaitu tidak

perlunya seseorang untuk memiliki banyak identitas dan

mengingat banyak ID. Cukup dengan menggunakan SIN,

penduduk dapat menggunakan berbagai macam layanan

pemerintahan yang berbeda-beda. Dibalik semua kemudahan

ini, terdapat suatu potensi ancaman keamanan jika SIN

disalahgunakan oleh orang lain. Untuk itu diperlukan suatu

mekanisme pengamanan agar SIN memang diberikan dan

dimiliki oleh orang yang tepat. Paper ini bertujuan untuk

menjelaskan suatu usulan mekanisme pengamanan SIN dengan

menggunakan sidik jari dan QR Code.

Index Terms—Computer Security, Identity, Authentication.

Fridh Zurriyadi Ridwan is with the R&D Centre of PT Telekomunikasi

Indonesia (PT TELKOM) in Jakarta, Indonesia. He can be reached at:

fridh@telkom.co.id.

Hariyo Santoso is with the Consumer Directorate of PT Telekomunikasi

Indonesia (PT TELKOM) in Jakarta, Indonesia. He can be reached at:

hariyo@telkom.co.id.

Dr. Wiseto Agung is with the R&D Centre of PT Telekomunikasi

Indonesia (PT TELKOM) in Jakarta, Indonesia. He can be reached at:

wiseto@telkom.co.id.

I. PENDAHULUAN

emerintah Indonesia telah mencanakan bahwa pada tahun

2012 akan diimplementasikan sistim Single Identity

Number (SIN) sebagai identitas tunggal penduduk Indonesia.

SIN ini adalah 16 digit angka yang digunakan sebagai

identitas unik dari seluruh penduduk Indonesia. Seperti

banyak diketahui, saat ini masyarakat mengantongi banyak

nomor identitas, mulai dari nomor Kartu Tanda Penduduk

(KTP), nomor Surat Ijin Mengemudi (SIM) dan lainnya.

Penerapan nomor identitas tunggal ini juga dimaksudkan

untuk memudahkan seseorang dalam setiap pengurusan

dokumen dan berbagai keperluan lainnya.[1] Dengan

diterapkannya SIN, diharapkan akan sangat banyak manfaat

yang diperoleh seperti diantaranya: tidak perlunya seseorang

untuk memiliki banyak identitas dan mengingat banyak ID.

Cukup dengan menggunakan SIN, penduduk sudah bisa

mendapatkan berbagai macam layanan pemerintahan yang

berbeda-beda. Selain itu permasalahan KTP ganda, informasi

pajak yang terintegrasi dan lain-lain juga dapat disolusikan

dengan menggunakan SIN. Akan tetapi dibalik semua

kemudahan ini, terdapat suatu potensi ancaman keamanan jika

SIN disalahgunakan oleh orang lain. Untuk itu diperlukan

suatu mekanisme pengamanan agar SIN memang diberikan

kepada dan dimiliki oleh orang yang tepat. Pada bagian

selanjutnya akan dibahas mengenai QR code dan sidik jari

yang diusulkan untuk digunakan sebagai pengaman dari SIN.

II. QR CODE

Quick Response (QR) code adalah sebuah kode matriks

dalam bentuk dua dimensi yang dikembangkan oleh

perusahaan Jepang Denso-Wave pada tahun 1994. Gambar 1

menunjukkan contoh bentuk QR Code.

Beberapa kelebihan dalam menggunakan QR Code adalah

sebagai berikut:

Dibandingkan dengan barcode dua dimensi lain yang ada

(misalnya: Datamatrix, PDF417 dan lain-lain) QR Code

mampu menyimpan informasi cukup banyak (maksimum

4296 karakter untuk alfanumerik atau 7089 digit numerik)

[3]. Sebagai ilustrasi, barcode satu dimensi standar yang

banyak digunakan hanya mampu menampung informasi

sebanyak 20 digit [4].

Memiliki kemampuan error correction. Data dapat

diperbaiki meskipun QR code mengalami kerusakan atau

Mengamankan Single Identity Number (SIN)

Menggunakan QR Code dan Sidik Jari

Fridh Zurriyadi Ridwan, Hariyo Santoso, dan Wiseto P. Agung

PT Telekomunikasi Indonesia

P

18 INTERNETWORKING INDONESIA JOURNAL RIDWAN ET AL.

ISSN: 1942-9703 / © 2010 IIJ

kotor sebagian.

Dapat dicetak di KTP dengan ukuran optimum sekitar

1,5cm x 1,5cm, dimana terdapat 57 x 57 modul yang

dapat menyimpan data hingga 400 karakter

(alphanumerik).

Proses pembacaan yang cepat karena tidak harus dibaca

dalam posisi sudut tertentu seperti halnya barcode satu

dimensi [4].

Standard (ISO/IEC18004) dan memiliki banyak reader

untuk terminal ponsel dan PC [5].

Mudah dibawa-bawa.

Lebih murah jika dibandingkan media penyimpanan

smartcard berbasis chip.

Gambar 1. QR Code

QR code saat ini banyak digunakan di Jepang. Di Indonesia

penggunaan QR code belum terlalu populer. Akan tetapi

aplikasi QR reader untuk berbagai macam tipe ponsel dan PC

cukup banyak tersedia untuk diunduh secara gratis melalui

Internet. Di Indonesia QR Code digunakan di beberapa artikel

dari harian Kompas untuk menyimpan URL dari artikel terkait

yang dapat diakses melalui aplikasi browser di ponsel.

III. SIDIK JARI

Sidik jari setiap manusia sifatnya adalah unik, sehingga

sidik jari merupakan alat yang dapat dipercaya untuk

memastikan bahwa SIN yang digunakan adalah dimiliki oleh

orang yang benar. Dengan menggunakan sidik jari, pengguna

juga tidak perlu mengingat SIN-nya atau kata kunci jika

dibutuhkan. Cukup dengan menyediakan informasi sidik

jarinya melalui mesin pembaca sidik jari, maka proses

otentikasi atas SIN dapat dilakukan dengan mudah. Sidik jari

juga dapat dikonversi ke dalam bentuk digital, sehingga dapat

disimpan dalam bentuk basis data RDBMS biasa. Dalam

menyimpan data sidik jari, biasanya sistem mentranslasikan

data sidik jari ke dalam bentuk numerik, sehingga dapat

disimpan dalam basis data. Beberapa sistem mentranslasikan

sidik jari ke dalam numerik dalam jumlah yang cukup banyak.

Untuk meringkasnya, dapat diterapkan mekanisme

pengkompresian dengan algoritma tertentu.

Sidik jari cukup sensitif terhadap akurasi pembacaan mesin

pembaca sidik jari, sehingga mungkin dibutuhkan beberapa

kali proses pembacaan (scanning) untuk melakukan proses

otentikasi. Saat ini sudah banyak mesin pembaca sidik jari

yang menggunakan terminal USB dan bentuknya cukup kecil,

sehingga memudahkan dalam proses instalasi dan mobilitas

jika dibutuhkan dengan menggunakan notebook sebagai

terminal pengganti PC.

IV. METODA IDENTIFIKASI PENDUDUK

Salah satu bentuk usulan pengamanan atas SIN adalah

dengan melakukan pemetaan informasi-informasi unik dari

seseorang dengan SIN yang diberikan. Informasi unik dari

seseorang biasanya menggunakan data biometrik yang umum

digunakan seperti sidik jari atau retina. Sidik jari saat ini lebih

banyak digunakan karena banyak reader yang tersedia di

pasaran dengan harga yang relatif murah. Informasi-informasi

unik yang akan digunakan dalam solusi ini diantaranya adalah:

Data sidik jari (fingerprint).

Nama.

Tempat & tanggal lahir.

Golongan Darah.

URL alamat dari data sidik jari orang tersebut.

Semua data-data tersebut harus dapat direlasikan 1:1 dengan

pemilik SIN dan dapat diakses dengan mudah untuk

melakukan verifikasi.

Untuk mendukung kebutuhan ini, maka sebagian data-data

tersebut disimpan dalam bentuk barcode 2 dimensi yaitu

menggunakan QR Code. Sedangkan khusus data sidik jari

akan disimpan dalam bentuk basis data.

Untuk lebih mengamankan informasi yang disimpan di

dalam QR Code, maka sebagian informasi (misalnya URL

alamat data sidik jari) dapat dienkripsi dengan menggunakan

algoritma yang dibuat khusus yang hanya dapat dibaca oleh

aplikasi yang dikembangkan khusus pula.

V. KONFIGURASI SISTEM

Di dalam konfigurasi sistem yang diusulkan, QR Code

digunakan untuk menyimpan SIN dan informasi-informasi

unik lainnya seperti tanggal lahir, golongan darah dan URL,

dengan parameter yang unik untuk setiap penduduk, untuk

mengakses basis data SIN.

Ada dua cara penambahan QR code yang diusulkan bagi

KTP yang saat ini digunakan penduduk. Cara pertama adalah

pembuatan QR Code dilakukan di setiap kantor Kelurahan dan

dicetak dengan ukuran yang cukup kecil agar dapat

ditempelkan/ditambahkan di kartu SIN/KTP. Cara kedua

adalah pada saat pembuatan kartu identitas baru, sudah

menyertakan QR Code yang telah dibuat. Selanjutnya QR

Code ini dapat dibaca dengan menggunakan ponsel yang

memiliki kamera atau PC dengan menggunakan web camera

yang sebelumnya telah diinstal aplikasi QR Reader.

Di dalam konfigurasi sistem yang diusulkan, data sidik jari

disimpan di dalam suatu basis data khusus yang dapat diakses

melalui jaringan privat virtual (VPN). Jaringan privat virtual

digunakan untuk menghubungkan kantor Kelurahan dengan

pusat data SIN. Dengan menggunakan jaringan privat virtual,

maka dapat dijaga keamanan komunikasi antara Kelurahan

dan pusat data SIN yang mungkin terpisah secara fisik dari

ancaman penyadapan informasi oleh pengguna yang tidak

terdaftar di dalam sistem SIN. Sidik jari ini diambil dari setiap

penduduk di Kantor kelurahan terdekat dan kemudian

direlasikan dengan SIN penduduk tersebut. Idealnya data sidik

jari ini dapat diakses secara nasional sehingga dapat

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ISSN: 1942-9703 / © 2010 IIJ

membantu proses verifikasi apakah seseorang sudah atau

belum pernah memiliki SIN atau identitas lainnya yang

dikeluarkan dari dearah lain.

Gambar 2 berikut menunjukkan konfigurasi dari sistem

yang diusulkan.

Gambar 2. Konfigurasi Sistem

Konten dari basis data SIN juga harus dijamin

keamanannya. Untuk itu informasi sidik jari yang disimpan di

dalam basis data harus disimpan di dalam format yang

terenkripsi (encrypted data). Sedangkan hubungan antara

kantor kelurahan dengan pusat data SIN direkomendasikan

menggunakan jaringan privat virtual. Untuk menjamin bahwa

hanya petugas dari kantor kelurahan yang berwenang saja

yang dapat mengakses pusat data SIN, maka proses otentikasi

dapat menyertakan juga MAC address dari terminal PC yang

digunakan oleh petugas Kelurahan.

VI. ALIRAN PROSES

Untuk mengimplementasikan sistem ini, aliran proses yang

diusulkan adalah sebagai berikut:

A. Pembuatan Kartu Identitas

1. Penduduk harus datang ke kantor Kelurahan dimana ia

tinggal untuk melakukan pengambilan data sidik jari.

Di kantor Kelurahan disediakan perangkat pembaca

sidik jari. Untuk orang-orang yang tidak mampu

datang ke kantor Kelurahan karena sakit atau cacat

maka petugas Kelurahan yang akan mendatangi orang-

orang tersebut dengan membawa komputer notebook

dan perangkat pembaca sidik jari.

2. Petugas akan memeriksa ke basis data SIN apakah

orang tersebut sudah memiliki SIN dengan

mencocokkan sidik jari yang diperolehnya dengan

basis data sidik jari yang sudah ada.

3. Jika ditemukan sidik jari yang cocok dan orang

tersebut sudah memiliki SIN, maka petugas Kelurahan

tidak dapat membuat kartu identitas baru dengan SIN

baru untuk orang tersebut.

4. Jika tidak ditemukan sidik jari yang cocok, maka data

sidik jari yang diperoleh kemudian disimpan di dalam

basis data SIN dan direlasikan dengan SIN orang

tersebut.

5. Petugas kependudukan kemudian membuat QR code

yang berisi informasi-informasi unik dari orang

tersebut.

6. Selanjutnya petugas kependudukan membuat kartu

identitas yang menyertakan QR code tersebut.

B. Pengecekan Kartu Identitas

1. Penduduk diharuskan untuk menunjukkan kartu

identitas untuk mengajukan suatu layanan masyarakat

(misalnya untuk mengajukan permohonan IMB).

2. Dengan menggunakan aplikasi QR Reader yang

diinstal di PC atau di ponsel, Petugas Kelurahan atau

petugas dari Instansi terkait akan membaca QR code

yang ada pada kartu identitas untuk mendapatkan

informasi unik yang dibutuhkan.

3. Jika dibutuhkan untuk otentikasi lebih lanjut, maka

petugas dapat meminta orang tersebut untuk

mencocokkan sidik jarinya dengan basis data sidik jari.

Aplikasi QR Reader akan membaca data URL pada

QR code milik orang tersebut yang berisi alamat untuk

mengakses informasi sidik jari dari orang tersebut.

VII. BISNIS PROSES PENUNJANG

Untuk mengoptimalkan pemanfaatan QR code dan sidik jari

dalam sistem SIN, diperlukan beberapa hal lain sehingga

secara kesisteman SIN dapat digunakan untuk banyak

keperluan dengan mudah dan tidak membutuhkan proses

bisnis yang rumit.

QR code yang berisi SIN dan sidik jari harus dicetak

dalam Kartu Tanda Penduduk (KTP).

Tersedia QR code dalam bentuk softcopy, sehingga

transaksi dapat dilakukan secara online atau dengan

menggunakan perangkat portabel seperti ponsel. Dengan

semakin mudahnya penyimpanan dan proses yang tidak

rumit, masyarakat akan lebih terbiasa dengan

pemanfaatan SIN.

Dapat diintegrasikan dengan sistem pelayanan satu atap.

VIII. PENUTUP

Penggunaan QR Code dan data sidik jari diharapkan dapat

membantu pengamanan SIN yang akan diimplementasikan

agar tidak disalahgunakan oleh orang yang tidak berhak untuk

mengakses data-data atau layanan-layanan pribadi dari

pemilik SIN. Penggunaan QR code merupakan salah satu

solusi yang relatif murah jika dibandingkan dengan

menggunakan smartcard berbasis chip. Untuk implementasi

dalam skala nasional, perlu dipertimbangkan agar konfigurasi

pusat data SIN didesain dengan arsitektur terdistribusi,

20 INTERNETWORKING INDONESIA JOURNAL RIDWAN ET AL.

ISSN: 1942-9703 / © 2010 IIJ

sehingga waktu yang dibutuhkan untuk melakukan proses

pencocokan sidik jari dari tempat-tempat yang lokasinya

cukup jauh dengan pusat data SIN tetap seminimal mungkin.

QR Code juga dapat digunakan untuk menyimpan

informasi-informasi lainnya seperti untuk menyimpan

beberapa ID lain yang mungkin saja masih digunakan

bersama-sama dengan SIN selama masa transisi (misalnya

nomor KTP lama, NPWP atau SIM).

Pengembangan lebih lanjut yang dapat dilakukan adalah

pembacaan QR Code dan proses otentikasi sidik jari dapat

dilakukan melalui area publik dengan memanfaatkan jaringan

Internet yang tersedia atau melalui jaringan komunikasi

nirkabel yang tersedia (GSM, CDMA). Untuk hal ini perlu

dikaji lebih mendalam mengenai aspek pengamanan transaksi

data yang dilakukan.

Pengembangan lebih lanjut lainnya adalah jika metode ini

digunakan pada smartcard berbasis chip. Chip dapat

digunakan untuk menyimpan data-data sidik jari secara

offline, sehingga dapat mempercepat proses otentikasi sidik

jari jika dibandingkan dengan harus melakukan pengecekan

melalui jaringan privat karena tidak perlu melakukan koneksi

dan proses otentikasi ke jaringan privat terlebih dahulu.

DAFTAR PUSAKA

[1] “Kementrian PAN Mewujudkan Single Identity Number”, [Online].

Available: http://bataviase.co.id/detailberita-10574703.html, 29 Januari

2010, diakses: 6 Apr il 2010.

[2] “2010, Jakarta Terapkan „Single Identity Number‟" , [Online].

Available:

http://megapolitan.kompas.com/read/2009/09/25/12381886/2010..Jakart

a.Terapkan.Single.Identity.Number., 25 September 2009, diakses: 6

April 2010

[3] “QR Code”, [Online]. Available: http://en.wikipedia.org/wiki/QR_Code.

[4] About QR-Codes, [Online]. Available: http://www.mobile-

barcodes.com/about-qr-codes/#barcodes-vs-qr-codes

[5] “QR Code Standardization”, [Online]. Available: http://www.denso-

wave.com/qrcode/qrstandard-e.html, diakses: 6 April 2010

[6] “QR Code Features”, [Online]. Available: http://www.denso-

wave.com/qrcode/qrfeature-e.html, diakses: 7 April 2010

[7] “Fingerprint”, [Online]. Available:

http://en.wikipedia.org/wiki/Fingerprint

Fridh Zurriyadi Ridwan received the Informatical

Engineering degree from the TELKOM School of

Technology in Bandung, Indonesia in 1997 and a

Master of Information Technology degree from the

Computer Science Faculty of the University of

Indonesia (UI) in 2008. From 1997 to 2008 he

worked as an engineer in the R&D Center of PT

Telekomunikasi Indonesia (TELKOM). He is

currently working as a researcher in the TELKOM

R&D Center. His research interest are mostly

related with telecommunication services and

products. He is also actively involved in several

Asia Pacific Telecommunity (APT) joint research

work with researchers from Japan.

Hariyo Santoso received his Electrical

Engineering degree from the TELKOM School of

Technology in Bandung, Indonesia in 1997 and a

Master of Information Technology degree from the

Computer Science Faculty of the University of

Indonesia (UI) in 2009. From 1997 to 2009 he

worked as an engineer and researcher within the

R&D Center of PT Telekomunikasi Indonesia

(TELKOM). He is currently working in the

TELKOM Consumer Directorate as a Senior

Officer of the Device & CPE Product Development

and Deployment. He also involved in several Asia

Pacific Telecommunity (APT) joint research work

with researchers from Japan.

Dr Wiseto Agung received the BSc degree in

Telecommunications from Institut Teknologi

Bandung (ITB), Indonesia in 1987. He also

received an MSc degree in Telematics (in 1994)

and a PhD in Multimedia Communication (in 2002)

from the University of Surrey, UK. He has been

with PT Telekomunikasi Indonesia since 1988 in

various engineering divisions, and he is currently

working in the TELKOM R&D Centre as a Senior

Manager of Service and Product R&D. Within the

Asia Pacific Telecommunity (APT) Wireless

Forum (AWF) he holds the responsibility of the

Convergence Working Group Chairman.

Vol.2/No.2 (2010) INTERNETWORKING INDONESIA JOURNAL 21

ISSN: 1942-9703 / © 2010 IIJ

Abstract—In this research work we have developed a tool and

a program that can be used to replace the behavior of a mouse. Our results have shown that it can be used successfully to replace a mouse that is usually operated by hand. The system we developed in this research can be used to help people with quadriplegia (paralyzed) to operate a computer. In order to be highly accessible and affordable, the tool we built should be inexpensive and can be purchased easily. Thus we have selected an input method using a series of LEDs and a webcam. There are two LED used on the circuit due to the need to have two light sources in order to replace the mouse behavior. The image captured by the webcam is then filtered using an image segmentation technique (thresholding). The first light source is used as the head-tilt detector that will be used as the replacement for the left-button and right-button of the mouse. The second light source is used as a head-motion detector, to replace the mouse movement. Our head movement tracking device uses two pieces of LEDs, wires, several resistors and energy source (battery).

Index Terms— head movement tracking device, image segmentation, mouse controlling, thresholding.

I. INTRODUCTION

person who suffers quadriplegia is unable move his or her arms and legs to operate a mouse and keyboard [7].

Therefore, in order for such a person to operate today’s computers there is a need for special tools to aid that person operating a computer.

Recently, there has been a number of special equipment and software that has been developed to help a people who suffer from quadriplegia to interact with a computer. However, most of these specialized equipment and software is difficult to obtain. The difficulty is mostly due to their high

FX. Ferdinandus, Tri Kurniawan Wijaya, and Indra Maryati are with the

Department of Computer Science, Sekolah Tinggi Teknik Surabaya, Surabaya 60284, Indonesia They can be contacted at email: {ferdi, tritritri}@stts.edu, and indra.maryati@gmail.com.

Gunawan is with the Department of Electrical Engineering, Faculty of Industrial Technology, Institut Teknologi Sepuluh Nopember, Surabaya 60111 Indonesia. He can be contacted at email: admin@hansmichael.com.

Edwin Seno Dwihapsoro is with the Department of Computer Science, Sekolah Tinggi Teknik Surabaya, Surabaya 60284 Indonesia.

prices (particularly for developing nations), the low availability of these instruments or items, and the regulations pertaining to the buying and selling of electronic goods across states. One example of a detector that recently became available is the “Ocular Mouse” which is an eye muscle detector. However, the tool is only available to certain communities and it is expensive. There is also a head movement tracking device called TrackIR that is relatively well known compared to other tracer devices. This tool is well known for its primary function to play games. However, such a tool that can detect head movements can also be used to help people who suffer from quadriplegia to operate a computer. However, similar to other head movement tracking devices on the market, the price of this tool is also relatively expensive and therefore unaffordable the majority of people in developing nations, including Indonesia.

II. IMAGE SEGMENTATION

Thresholding is a simple method of image segmentation. A thresholding method allows the creation of a binary image from a gray scale image [2]. A binary image is an image in which every pixel can have of one of two possible values. The values that are usually chosen are the values that represent black and white, although the values that represent other colors can also be used.

In the binary image, a pixel color value is used as the foreground/object color while the value of the other colors is used as the background. The ultimate purpose of the threshold method is to simplify the representation of the image so that the image can be more easily analyzed.

The reason a thresholding method was chosen is to allow us to more easily detect and differentiate between the features of tracked objects and the background. Since the thresholding method is a simple method, it is expected that required computational processes will not overload the system and that the basic specifications required for the system can be as minimum as possible.

EnsoTracker: Mouse Controller Device using Head Movement

Gunawan Institut Teknologi Sepuluh Nopember

FX. Ferdinandus, Tri Kurniawan Wijaya, Indra Maryati and Edwin Seno Dwihapsoro

Sekolah Tinggi Teknik Surabaya

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INT

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TERNETWORK

ISSN: 194

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KING INDONE

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ESIA JOURNAL

10 IIJ

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HITECTURE

ystem is shownfrom the head system emits livice.

WAN ET AL.

ess accurate. ality camera. is also bulky e movement his tool was

companies, bit systems, .

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INT

deo input devsensor devicesa format reprermat). To be

aptures images ding on the spngs allowed.

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TERNETWORK

ISSN: 194

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stem

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KING INDONES

42-9703 / © 201

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SIA JOURNAL

10 IIJ

device send dam data can be und card datars. program acce

h DSPack234Show [17]. (DS

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ments and actioments and actionoperate the nation of the oen keyboard cpplications suct.

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VI. ENSOTRA

we named ourd, Ensotrackew API. The soue in the form ofhe programs obam via DSPac

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of Ensotracker

receives the orsed on the ltation method.

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d binary image

put device. Fore it reachesssed before it

ge captured bys the image his research isses which canw). The imageelevant informput in the formn. In addition,can also be usenscreen keybhe program ansed to activatenet browser fo

ACKER

r system Ensoer gets the iurce image itsf a digital reprebtaining the orck234. The im

d in EnsotrackectShow API.

processes

riginal image, light intensity In this methoimage. Next, t using a thretracker, the trae) is done from

23

For example, the monitor, reaches the

y a webcam data from

a collection n be used to e is captured ation so that m of mouse , the mouse ed to activate board. The nd Windows e or operate

or surfing the

otracker. As image from elf is caught esentation of riginal image mage that is er systems is

the image is y using the od the initial the image is eshold value ansformation m the top-left

24

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mage acquired nsotracker prog

Figure 4: H

After the progrcond, the proowever Ensotrhen the light socause the progre input (see Fiad-tilt angle, wad position dennot be done we position and cptured image cssibility that te not of the hea

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θ = arctan ((X2

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e program has requires the dthe program ht pixel positione position. Thfrom the vide

gram is closed.

Head Movement

ram gets the fiogram can peracker cannot ource is more oram is designeigure 4). The fwhile the seco

etector. The reawith more thacondition of thcannot be knohe first light sad position mar

VII. ANGLE C

n in Figure 5, tfirst light soursource (box 2)

he known valux 2 (X1, X2, Y

2-X1) / (Y2-Y

rmula can leadnd Y of the boxove has a reY is at least 1.

INT

rdinate of the imlight source's cale image intocess, when the s the pixel posion. After that tositions until reached a certistance to be ad covered then, that positionhis process coeo input devic

Tracking Devic

irst light sourceerform the mperform the m

or less than twod to use two (2first light sourond light sourcason why the an two light sohe equipment aown. This is bsource positionrker tool, but is

CALCULATIONS

the angle θ is orce (box 1) of t). The formula ues are the X aY1, Y2) is:

1)) * 180/pi (1)

d to an error if x 1 and box 2. quirement thaSpecial attent

TERNETWORK

ISSN: 194

mage. position as

o a binary imaprogram findsition and storethe program dothe image that

tain distance. T20 pixels do

e distance, if thn is stored as

ontinues until ce is stopped

ce Ensotracker

e position and mouse emulatimouse emulato sources. This2) light sourcesrce is used as ce is used as mouse emulat

ources is becaut the head marecause there in and the secos instead a nois

S

obtained from the triangle A aused to calcul

and Y position

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KING INDONE

42-9703 / © 201

the age. s an s it oes t is The own here the the or

the on. ion s is s as the the ion use ker is a ond se.

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A nuactivitieThe resonly anrun.

The system either hEnsotrabrowse

ESIA JOURNAL

10 IIJ

en to the diffe“division by

t degree to radbut the defa

.

Fi

testing of theclick, Interne

tions using Ensing the internt browser applnk. In the testgoogle.com” an

est the runninged a notepad

d we asked usthe on-screen ames similar tos user to movethe screen. In r edges/sides o

vent the ball fThe movable

and is controllbelow the scre

right, and uponagain. umber of reses in the abovespondents wer

nd were not allo

results of thecan help peo

hands or feetacker system

through the In

L

rence in the vzero” error. F

dians, because ault result of

igure 5: Angle C

VIII. TESTI

e system is doet browsing, asotracker. et browsing ilication, enteriting process, t

and the link use

g of other appliand a game cser to type thkeyboard). Ino Arkanoid, we a board or plthe game a ba

of the screen. from droppinge board is localed by the useeen. The user

n hitting the bo

spondents were test, providingre asked to usowed to use ei

e test allowedople browse tht. Thus it cancan help peo

nternet.

GUNAW

value of Y, beFormula 180/pwe need the vthe arctan ca

Calculation

ING

one on severaand the runni

is started by ing the URL, the URL used ed was the “Ab

ications using called “Smashie letters “ABC

n smashing gamwhich is a simplank to the righall bounces coThe object of

g down the boated on the boer to prevent this able to movard the ball bo

re asked to pg us with somese the Ensotraither hands or

d us to concluhe Internet wn also be conople with qua

WAN ET AL.

ecause it can pi is used to value of θ in alculation is

al cases: the ing of other

opening the and clicking was simply

bout Google”

Ensotracker, ing”. On the CDEFGHIJ” me, the user

ple game that ht and to the ontinually on f the game is ottom of the ottom of the he ball from ve the board

ounces up the

perform the e test results. acker system feet in a test

ude that the without using ncluded that adriplegia to

Vo

filereswittherefThindbes

in canpoto appandbei

devmostrquprothemo

Fdevledandimnofor

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10

ol.2/No.2 (2010)

The respondene or playing “spondents are thout any probe respondents flective boardshus from the redeed help peosides the Intern

IX.

In addition to this research

n be used as sition of the obwork correctl

plied to its opd the backgroing tracked. In this researvice that can rovement devicong potentialadriplegia. Hooduced by the e quality of theouse emulationFor the futurevelop more cod, better electrod others. In ad

mproved using ise easily and r a more refine

CoderSource.nehttp://www.codGonzalez, RafaImage ProcessinTopik Primary chttp://en.wikipeTopik Visible Shttp://en.wikipeRedação Terra, Hardware & Sohttp://tecnologia2008. Tania Orsi, Sciehttp://www.scieularmouse.htm.Quadriplegia - Qhttp://www.spinUN Enable - Rehttp://www.un.oMarket share fohttp://marketsha

0] Jow Webcams Wweb-cams-work

nts had no dif“Smashing” gaable to type i

blems. In tryindid not expe

s on the screenesults it can beople to run anet browser, w

CONCLUSION A

creating the hwe also showthe detecting

bject/light. Holy there are sperating enviroound should b

rch, we have replace the mouce in an inexls primarily owever, the rvideo input dee tracking of thn. e developmentonvenient toolsonic cables, adddition, the liga more robustcan use a largd the movemen

REFER

et, Binary Image, dersource.net/csharel C. & Woods, Rng. Pearson Educacolor Wikipedia, edia.org/wiki/PrimSpectrum Wikipediedia.org/wiki/VisibBrasileiro cria mo

oftware, a.terra.com.br/inte

enceNET, encenet.com.br/bac 2008. Quadriplegia, nalinjury.net/quadrelationship betweeorg/disabilities/defor browsers, operatare.hitslink.com/reWork, http://wwwk/. 2008.

INT

fficulty in eitheame. When tryin the letters g the game “Serience problen by using th

e concluded thaand operate oithout using th

AND FUTURE W

ead movementwed that a thre

and trackingwever, in orde

some constrainonment. Notabbe darker than

succeeded in use input using

xpensive way. for people

resolution andvice is very strhe head movem

s of our systes for the usersdjustable ear poght positioningt algorithm thager image resolnt of the mous

RENCES

rp_color_image_toichard E., Thresho

ation. 2002, pp. 59

mary_color. 2008. ia, ble_Spectrum. 200ouse ocular para t

erna/0,,OI1124734

ckup/english/scien

riplegia.htm. 2008en Development anfault.asp?navid=35ting systems and seport.aspx?qprid=1

w.digitalmoviebox.c

TERNETWORK

ISSN: 194

er opening a tying notepad, “ABCDEFGH

Smashing” whiems in operate mouse pointat the system cther applicatio

heir hands or fe

WORK

t tracking deviesholding methg method for er for this methnts that must bly ambient lin the object/li

implementingg a head trackThe device hsuffering fr

d image qualrongly influencments and by

em, the aim is, such as smalositioning handg process can at can handle lution that alloe pointer.

o_binary.aspx. 200olding. In Digital 5-611.

08. tetraplégicos - Ter

4-EI4801,00.html.

ncenews/ed_48/48_

8. nd Human Rights, 5&pid=33. 2008.earch engines, 10. 2008. com/mycamcar/ho

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42-9703 / © 201

text the

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eet.

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hod be

ght ght

g a ing has om lity ced the

s to ller dle,

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08.

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[11] Dev200

[12] BrayUseMic

[13] MSDus/li

[14] Oren(APhttpeBa

[15] Tophttp

[16] Paghttp

[17] Thehttp

SIA JOURNAL

10 IIJ

vice Driver Wikipe8. y, Andrew C.; Dic

er Guide for the BBcrocomputer CentrDN, DirectShow dibrary/ms783323.anstein, David. “Qu

PI)". Computerworp://www.computerwasic&articleId=434pik Augmented Rep://en.wikipedia.orge 1 Introduction top://www.vwlowen.e DSPack Project. pp://www.progdigy.

GSTSdNwSSLIMA

FCTCTISTi

TdTtyDTDiTlst

ICTpTSLI

edia, http://en.wiki

ckens, Adrian C.; HBC Microcomputere. 1983, pp. 442-4documentation. httaspx. 2007. uickStudy: Applicrld. rworld.com/action/487. 2000. eality Wikipedie, rg/wiki/Augmentedo DirectShow, .co.uk/directshow/prodigy.com. .com/?page_id=4.

Gunawan receiveScience in 1991Technology in 20Surabaya, Indonedoctoral degree atNopember, in Surworking as Vice DSenior Lecturer Surabaya. His reLanguage ProceIndonesia), ArtificMining, Neural Algorithm.

F. X. FerdinanduComputer ScienceTeknik Surabaya, Computer SciencTeknologi SepulIndonesia. He is cuStudent Affairs anTinggi Teknik Sinclude Networkin

Tri Kurniawan degree in ComputTinggi Teknik Surtime computer labyear of study in Department of CoTinggi Teknik SuDecember 2007. Cin the faculty of ITechnology. His rlearning, neural systems, intelligentheory.

Indra Maryati rComputer ScienceTeknik Surabayapursuing a maTechnology at Surabaya. AdditioLecturer at the SekIndonesia.

ipedia.org/wiki/De

Holmes, Mark A. er. Cambridge, UK443. tp://msdn.microso

cation Programmin

/article.do?comma

d_reality. 2008.

/page01.htm. 2008

2008.

ed the S.Kom degr1 and M.Kom 005 from Sekolahsia. Currently het the Institut Tek

rabaya, Indonesia. Dean of Academi

at Sekolah Tsearch interests i

essing (especiallycial Intelligence, Network, Fuzzy

us received the S.e in 1992 from Indonesia, and the

ce in 1997 fromluh Nopember, urrently working and a Senior Lectu

Surabaya. His resng, and Data Encry

Wijaya receivedter Science in 200rabaya, Indonesia.

boratory assistant sthis university.

omputer Science,urabaya, as a facuCurrently, he is a gInformatics, Viennresearch interests i

networks, decnt agent, data min

received the S.Ke in 2010 from thea, Indonesia. Custer’s degree ithe Sekolah T

onally, she is workkolah Tinggi Tekn

25

evice_driver.

The Advanced K: Cambridge

ft.com/en-

ng Interface

and=viewArticl

8.

ree in Computer in Information

h Tinggi Teknik e is pursuing a knologi Sepuluh

He is currently ic Affairs and a Tinggi Teknik include Natural y in Bahasa Data and Web

y, and Genetic

.Kom degree in Sekolah Tinggi e M.T degree in m the Institut

in Surabaya, as Vice Dean of urer at Sekolah search interests yption.

d his bachelor 07 from Sekolah . He was a full-since his second

He joined the at the Sekolah

ulty member in graduate student na University of include machine cision support ning, and game

Kom degree in Sekolah Tinggi

urrently she is in Information Tinggi Teknik king as a Junior nik Surabaya, in

26

Edwin Sendegree in CoTinggi Tekndegree in EcWijaya Putrathe IT ManaContactor Co

INT

o Dwihapsoro reomputer Science inik Surabaya, Indconomics in 2008 a, Surabaya. He is

agement division aompany.

TERNETWORK

ISSN: 194

eceived the S.Kin 2008 from Sekodonesia, and the

from the Universs currently workint Magna Karsa Mu

KING INDONE

42-9703 / © 201

Kom olah S.E

sitas g in ulya

ESIA JOURNAL

10 IIJ

L GUNAWWAN ET AL.

Vol.2/No.2 (2010) INTERNETWORKING INDONESIA JOURNAL

ISSN: 1942-9703 / © 2010 IIJ

27

Abstract—Sanata Dharma University has been developing

a web based learning management system, named Exelsa

(Experiential E-Learning of Sanata Dharma University)

since 2008. Exelsa provides a number of learning facilities,

including an online discussion board, lecture materials,

course content management, a course calendar/schedule,

information announcement, online test, auto-marked

quizzes and exams. The research aims to analyze the most

dominant factors that underlie the acceptance and use of

Exelsa among the students of Sanata Dharma University

by adopting UTAUT model (Venkatesh et al. in 2003). The

data is gathered by distributing questionnaires to many

course members and by collecting data from the database

of Exelsa. After the data was tabulated then it was

analyzed using Partial Least Square (PLS). The results

shows that performance expectancy, social influence and

facilitating conditions have significant influence (α = 0.05)

on behavioral intention. It was also shown from two

predictor of use behavior (behavioral intention and

facilitating conditions), the behavioral intention has a

significant influence on use behavior. The research model

explained 27.3% of the variance in user intention to use

Exelsa. Based on this results, it can be concluded that the

UTAUT model is insufficient in explaining students’

intentions in using LMS.

Index Terms—Exelsa, LMS, PLS, UTAUT.

I. INTRODUCTION

ejak diperkenalkannya Learning Management System

(LMS), cara staf pengajar bekerja telah berubah pesat.

Apakah pelajaran diajarkan sepenuhnya online atau

apakah menggunakan paduan pendekatan tradisional dan

online, sebagian besar staf pengajar di universitas harus

merancang dan mengembangkan materi online, membuat dan

memelihara situs web pembelajaran dan LMS telah menjadi

sarana komunikasi yang dominan dengan siswa bagi banyak

staf pengajar [12]. Definisi dari LMS adalah infrastruktur dimana e-Learning

dapat dibangun dan diantarkan [10]. Sementara e-Learning

adalah proses pembelajaran yang memanfaatkan teknologi

I Gusti Nyoman Sedana adalah staf quality assurance di PT. Sigma Cipta

Caraka. Penulis dapat dihubungi melalui email, pandji_jaya@yahoo.com.

St. Wisnu Wijaya adalah Dosen Teknik Infromatika Universitas Sanata

Dharma Yogyakarta. Penulis dapat dihubungi di stevanus.wisnu@usd.ac.id.

informasi dan komunikasi secara sistematis dengan mengintegrasikan semua komponen pembelajaran, termasuk

interaksi pembelajaran lintas ruang dan waktu, dengan kualitas

yang terjamin [17]. Pengembangan sistem pembelajaran

berbasis digital (LMS) merupakan salah satu kegiatan untuk

meningkatkan kualitas pembelajaran di Universitas Sanata

Dharma [18]. Kegiatan ini diadakan untuk mendukung salah

satu sasaran jangka pendek Universitas Sanata Dharma yaitu,

peningkatkan efisiensi dan produktifitas. Untuk mencapai

sasaran ini, sejak tahun 2008 Universitas Sanata Dharma telah

mengembangkan dan mengimplementasikan sebuah LMS

yang diberi nama Experiential E-Learning of Sanata Dharma

University, yang lebih dikenal dengan nama Exelsa [13]. Proyek ini diikuti oleh kebijakan Universitas dengan

mengadakan pelatihan penggunaan Exelsa dan pengembangan

materi pembelajaran digital bagi para dosen [18]. Disamping

itu, juga dilakukan sosialisasi mengenai keberadaan Exelsa

melalui penyebaran brosur-brosur di lingkungan Universitas

Sanata Dharma.

Sebagai sebuah media pembelajaran, Exelsa harus dapat

diterima dan digunakan oleh para penggunanya sehingga

dapat meningkatkan produktivitas. Salah satu model terbaru

untuk menjelaskan penerimaan pengguna (user acceptance)

dalam bidang Sistem Informasi dikembangkan oleh Venkatesh, dkk. [20]. Model ini diberi nama the Unified

Theory of Acceptance and Use of Technology (UTAUT).

UTAUT menunjukkan bahwa niat untuk berperilaku

(behavioral intention) dan perilaku untuk menggunakan suatu

teknologi (use behavior) dipengaruhi oleh persepsi orang-

orang terhadap ekspektansi kinerja (performance expectancy),

ekspektansi usaha (effort expectancy), pengaruh sosial (social

influence) dan kondisi yang membantu (facilitating

conditions) yang dimoderatori oleh jenis kelamin (gender),

usia (age), pengalaman (experience) dan kesukarelaan

(voluntariness). Teori ini menyediakan alat bagi para manajer

untuk menilai kemungkinan keberhasilan pengenalan teknologi baru dan membantu mereka memahami penggerak

penerimaan dengan tujuan untuk proaktif mendesain

intervensi (termasuk pelatihan, sosialisasi, dll.) yang

ditargetkan pada populasi pengguna yang mungkin cenderung

kurang untuk mengadopsi dan menggunakan sistem baru [20].

Model asli UTAUT divalidasi menggunakan data yang

dikumpulkan dari lingkungan non akademik. Meskipun

demikian, beberapa peneliti telah menerapkan model ini di

lingkungan akademik. Dasgupta, dkk. [4] menerapkan model

UTAUT untuk memahami persepsi mahasiswa terhadap

UTAUT Model for Understanding Learning

Management System

I Gusti Nyoman Sedana

Universitas Sanata Dharma Yogyakarta, Indonesia

St. Wisnu Wijaya

Universitas Sanata Dharma Yogyakarta, Indonesia

S

28 INTERNETWORKING INDONESIA JOURNAL SEDANA & WIJAYA

ISSN: 1942-9703 / © 2010 IIJ

penerimaan dan penggunaan Case tools. Hasilnya effort

expectancy tidak berpengaruh terhadap behavioral intention.

Marchewka, dkk. [11], Sedana dan Wijaya [14] juga

melaporkan adanya perbedaan dengan model asli UTAUT

ketika mereka menerapkan UTAUT di lingkungan akademik.

Menurut laporan Marchewka, dkk. [11], performance expectancy tidak memiliki hubungan yang signifikan dengan

behavioral intention. Sementara dalam laporan studi

pendahuluan yang dilakukan oleh Sedana dan Wijaya [14],

effort expectancy tidak memiliki hubungan yang signifikan

dengan behavioral intention. Sedana dan Wijaya [14] juga

melaporkan bahwa facilitating conditions memiliki hubungan

yang signifikan dengan behavioral intention.

Meskipun hasil penelitian-penelitian dengan model

UTAUT di lingkungan akademik sedikit berbeda dengan

model aslinya, kami percaya dengan mengadopsi model

UTAUT akan membantu kami untuk memahami faktor-faktor

yang mendasari penerimaan dan penggunaan Exelsa. Namun untuk menyesuaikan dengan situasi dan kondisi lingkungan

penelitian, dalam penelitian ini kami tidak menggunakan

variabel moderator.

II. EXELSA

Experiential E-Learning of Sanata Dharma University

(Exelsa) adalah sebuah Learning Management System (LMS)

berbasis web yang dikembangkan untuk meningkatkan kualitas pembelajaran dan pendidikan di Universitas Sanata

Dharma [13]. Sebagai sebuah LMS, Exelsa menyediakan

sejumlah fasilitas pembelajaran seperti, tugas online, tes

online, bahan kuliah, chating, menjawab kuesioner, melihat

pengumuman, forum mata kuliah, kalender kegiatan dan

sebagainya. Lebih lanjut, Exelsa diharapkan dapat

meningkatkan efektifitas dan kualitas komunikasi

pembelajaran dengan pendekatan knowledge management

diantara berbagai pihak seperti dosen, mahasiswa, program

studi, biro administrasi akademik, penyedia media

pembelajaran serta berbagai pihak lainnya yang

berkepentingan [13]. Penggunaan Exelsa di Universitas Sanata Dharma tidak

bersifat wajib. Meski demikian, beberapa jurusan seperti,

Teknik Informatika, Teknik Mesin, Teknik Elektro,

Pendidikan Akuntansi dan Bimbingan dan Konseling telah

memanfaatkannya. Para mahasiswa dari jurusan-jurusan ini

memiliki pengalaman yang relatif sama dalam menggunakan

Exelsa. Usia mereka pun tidak terpaut jauh antara satu dengan

yang lainnya. Alasan-alasan inilah yang menyebabkan kami

tidak menggunakan variabel moderator dalam penelitian ini.

III. UTAUT

The Unified Theory of Acceptance and Use of Technology (UTAUT) merupakan salah satu model penerimaan teknologi

terkini yang dikembangkan oleh Venkatesh, dkk. [20].

UTAUT mensintesis elemen-elemen pada delapan model

penerimaan teknologi terkemuka untuk memperoleh kesatuan

pandangan mengenai penerimaan pengguna. Kedelapan teori

terkemuka yang disatukan di dalam UTAUT adalah theory of

reasoned action (TRA), technology acceptance model (TAM),

motivational model (MM), theory of planned behavior (TPB), combined TAM and TPB, model of PC utilization (MPTU),

innovation diffusion theory (IDT) dan social cognitive theory

(SCT). UTAUT terbukti lebih berhasil dibandingkan

kedelapan teori yang lain dalam menjelaskan hingga 70 persen

varian niat (intention) [14].

Model UTAUT memiliki empat konstruk yang memainkan

peran penting sebagai determinan langsung dari behavioral

intention dan use behavior yaitu, performance expectancy,

effort expectancy, social influence, dan facilitating conditions

(definisinya dapat dilihat pada tabel 1). Disamping itu terdapat pula empat moderator: gender, age, voluntariness, dan

experience yang diposisikan untuk memoderasi dampak dari

konstruk-konstruk pada behavioral intention dan use behavior.

Gambar 1. Model UTAUT.

Sumber: Venkatesh, dkk. [20].

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29

Gambar 1 menampilkan keterkaitan antara determinan-

determinan dan moderator-moderator ini.

IV. METODOLOGI

Penelitian ini menggunakan model UTAUT yang telah

dimodifikasi sedemikian rupa hingga menjadi lebih sederhana

seperti terlihat pada gambar 2.

Teknik pengambilan data dalam penelitian ini adalah

survei dan pengambilan data melalui basis data Exelsa.

Pengambilan sampel menggunakan metode purposive

sampling dengan kriteria: (1) kelas kuliah yang dipilih adalah kelas yang memanfaatkan Exelsa dalam proses pembelajaran,

(2) responden adalah mahasiswa Universitas Sanata Dharma

yang mengikuti kuliah yang telah ditentukan pada saat

penelitian. Data yang diperoleh dari survei adalah data primer

sedangkan, pengambilan data perilaku penggunaan Exelsa dari

basis data menghasilkan data sekunder. Data primer adalah

data yang diperoleh langsung di lapangan. Sedangkan data

sekunder adalah data yang diperoleh dari sumber-sumber yang

telah ada [6].

Instrumen penelitian yang dipakai adalah skala UTAUT.

Skala UTAUT diadaptasi dari skala penelitian Venkatesh

dkk.[20]. Skala ini mencakup lima aspek yaitu, performance expectancy (PE), effort expectancy (EE), social influence (SI),

facilitating conditions (FC), dan behavioral intention (BI).

Model penilaian pernyataan terdiri dari lima alternatif jawaban

yang diberi skor berkisar dari 1 sampai 5. Klasifikasi

jawabannya adalah Sangat Tidak Setuju (STS), Tidak Setuju

(TS), Netral (N), Setuju (S) dan Sangat Setuju (SS).

Sementara itu, konstruk use behavior diukur menggunakan

frekuensi login mahasiswa dalam 5 minggu. Frekuensi login

diperoleh dengan cara mengolah data dari basis data Exelsa

milik Lembaga Penjaminan Mutu Universitas Sanata Dharma.

Sejumlah 232 mahasiswa dari berbagai program studi mengisi kuesioner, dari jumlah tersebut 214 kuesioner

dinyatakan layak untuk dianalisis sedangkan sisanya tidak

layak untuk dianalisis karena tidak valid. Pengambilan data

kemudian dilanjutkan dengan mengambil data dari basis data

Exelsa berdasarkan nomor induk mahasiswa (NIM) yang telah

mengisi kuesioner dengan valid. Data ini kemudian diolah

sehingga diperoleh hasil berupa frekuensi login mahasiswa

selama lima minggu. Rentang waktu lima minggu tersebut

merupakan masa dimana mahasiswa akan, sedang, dan sudah

menempuh ujian tengah semester.

V. HASIL PENELITIAN DAN PEMBAHASAN

Dari 214 responden yang telah mengisi kuesioner dengan valid, 107 responden adalah laki-laki dan sisanya adalah

perempuan. Data yang telah terkumpul kemudian ditabulasi

lalu dianalisis dengan menggunakan partial least square

(PLS) dengan bantuan perangkat lunak SmartPLS versi 2.0

M3. Chin (1998) dalam Henseler, dkk. [7] memberikan

kriteria untuk mengevaluasi model PLS dengan 2 langkah,

yang mencakup (1) evaluasi terhadap outer model dan (2)

evaluasi terhadap inner model.

A. Evaluasi Terhadap Outer Model

Outer model dengan indikator refleksif dievaluasi dengan

convergent dan discriminant validity dari indikatornya dan

composite reliability untuk blok indikator. Convergent

validity dinilai berdasarkan korelasi antara skor item dengan

skor konstruk yang dihitung dengan PLS [5], [7]. Tabel 2

memperlihatkan bahwa semua item memiliki loading factor di

atas 0,70 sehingga memiliki convergent validity yang baik [5],

[7].

Tabel 3

Composite

Reliability.

Composite

Reliability

BI 0,953

EE 0,847

FC 0,842

PE 0,869

SI 0,844

Gambar 2. Model Penelitian.

Tabel 4

Korelasi Variabel Laten dan Akar AVE.

Tabel 2

Loading Factor dari SmartPLS.

Performance

Expectancy

Effort

Expectancy

Social

Influence

Facilitating

Condition

Behavioral

Intention

Use

Behavior

30 INTERNETWORKING INDONESIA JOURNAL SEDANA & WIJAYA

ISSN: 1942-9703 / © 2010 IIJ

Discriminant validity dinilai dengan membandingkan nilai

square root of average variance extracted (akar kuadrat AVE)

setiap konstruk dengan korelasi antara konstruk dengan

konstruk lainnya dalam model. Dari tabel 4 dapat diketahui

bahwa nilai discriminant validity tergolong baik karena nilai

akar kuadrat AVE setiap konstruk lebih besar daripada nilai korelasi antara konstruk dengan konstruk lainnya dalam model

[5], [7].

Masing-masing konstruk dalam penelitian ini memiliki

reliabilitas yang tinggi. Hal ini dapat dilihat dari tabel 3

dimana composite reliability memiliki nilai yang tinggi (di

atas 0,80) [5], [7].

B. Evaluasi Terhadap Inner Model

Pengujian terhadap inner model dilakukan dengan melihat

nilai R-square yang merupakan uji goodness-fit model. Dari

tabel 5 dapat diketahui bahwa model penelitian ini

memberikan nilai R-square sebesar 0,273 dan 0,055. Hasil ini

dapat diinterpretasikan sebagai berikut, (1) besarnya varian

konstruk behavioral intention yang dijelaskan oleh varian

konstruk performance expectancy, effort expectancy, social

influence, dan facilitating conditions adalah 27,3%

sedangkan sisanya dijelaskan oleh variabel lain di luar yang diteliti. (2) besarnya varian konstruk use behavior (USE) yang

dijelaskan oleh varian konstruk facilitating conditions dan

behavioral intention adalah 5,5% sedangkan sisanya

dijelaskan oleh variabel lain di luar yang diteliti.

Tabel 5

R Square.

Variabel

Independen

Variabel

Dependen

R

Square

PE

BI 0,273 EE

SI

FC

FC USE 0,055

BI

Pengujian kemudian dilanjutkan dengan melihat signifikansi pengaruh variabel independen terhadap variabel

dependen dengan melihat nilai signifikansi statistik t dan nilai

koefisien parameter. Dari tabel 6 dapat diketahui bahwa

performance expectancy, social influence, dan facilitating

conditions memiliki pengaruh signifikan terhadap behavioral

intention karena nilai t hitung lebih besar daripada nilai t

tabel (1,97) pada taraf signifikansi sebesar 0,05. Behavioral

intention juga memiliki pengaruh signifikan terhadap use

behavior. Sementara effort expectancy tidak memiliki

pengaruh yang signifikan terhadap behavioral intention begitu

pula facilitating conditions tidak memiliki pengaruh yang

signifikan terhadap use behavior. Nilai koefisien parameter pengaruh performance

expectancy, social influence, dan facilitating conditions

terhadap behavioral intention secara berturut-turut adalah

0.147, 0.188, 0.322. Nilai koefisien parameter pengaruh

behavioral intention terhadap use behavior adalah 0,244.

C. Pembahasan

Temuan bahwa performance expectancy memberikan

pengaruh yang signifikan pada behavioral intention

mendukung hasil penelitian Venkatesh, dkk. [20], Dasgupta, dkk. [4]. Responden menganggap bahwa penggunaan Exelsa

dapat menolongnya untuk mendapatkan keuntungan-

keuntungan kinerja di pekerjaannya seperti, lebih mudah dan

cepat dalam mengerjakan dan menyelesaikan tugas-tugas

kuliah. Anggapan ini akan meningkatkan niatnya untuk

menggunakan Exelsa.

Tingkat kemudahan terkait penggunaan suatu sistem akan

mempengaruhi niat untuk menggunakan sistem tersebut [20],

[21]. Penelitian ini menemukan hasil yang berbeda yaitu,

tingkat kemudahan terkait penggunaan suatu sistem (effort

expectancy) tidak memberikan pengaruh yang signifikan pada behavioral intention. Dasgupta, dkk. [4], yang meneliti

tentang penerimaan pengguna terhadap Case Tools dalam

menganalisis dan mendesain suatu sistem, juga menemukan

pengaruh yang tidak signifikan seperti ini. Pengaruh yang

tidak signifikan ini dapat disebabkan karena Exelsa relatif

mudah digunakan dan berdasarkan hasil wawancara singkat

diperoleh informasi bahwa sebagian besar responden telah

memperoleh pengetahuan mengenai teknologi informasi dan

komunikasi sejak duduk di bangku Sekolah Menengah Atas.

Sehingga, responden tidak menganggap bahwa kemudahan

dalam menggunakan Exelsa akan mempengaruhi niatnya

untuk menggunakan Exelsa. Social Influence memiliki pengaruh yang signifikan

terhadap behavioral intention. Temuan ini mendukung hasil

penelitian Venkatesh, dkk. [20] dan dasgupta, dkk. [4]. Hasil

ini menunjukkan bahwa lingkungan sosial di sekitar responden

seperti teman kuliah, dosen, dan universitas mempengaruhi

niat mereka untuk menggunakan Exelsa.

Variabel facilitating conditions ternyata memiliki

pengaruh signifikan terhadap behavioral intention namun

tidak memiliki pengaruh yang signifikan terhadap use

behavior. Dasgupta, dkk. [4] juga menemukan bahwa

facilitating conditions memiliki pengaruh yang signifikan pada behavioral intention. Temuan ini berbeda dengan hasil

yang dilaporkan oleh Venkatesh, dkk. [20] dimana dalam

laporannya dikatakan bahwa facilitating conditions tidak

memiliki pengaruh yang signifikan terhadap behavioral

intention namun memiliki pengaruh yang signifikan terhadap

Tabel 6

Path Coefficients (Mean, STDEV, T-Values).

Vol.2/No.2 (2010) INTERNETWORKING INDONESIA JOURNAL

ISSN: 1942-9703 / © 2010 IIJ

31

use behavior. Tampaknya ketersediaan infrastruktur teknik

dan organisasional mendukung niat untuk menggunakan

Exelsa tapi tidak mendukung perilaku penggunaan Exelsa.

Lebih lanjut, hasil ini dapat disebabkan oleh lingkungan

dimana penelitian ini diadakan. Penelitian ini diadakan di

lingkungan akademik sedangkan model asli UTAUT divalidasi dengan data yang diperoleh dari lingkungan non

akademik. Mengenai tidak signifikannya pengaruh facilitating

conditions terhadap use behavior dapat disebabkan karena

tidak digunakannya moderator experience dan age. Seperti

yang diungkapkan oleh Venkatesh, dkk. [20] konstruk

facilitating conditions apabila dimoderasi oleh experience dan

age maka akan memiliki pengaruh yang signifikan pada use

behavior. Namun karena responden dalam penelitian ini

tergolong dalam satu kelompok usia yang sama, dan semuanya

memiliki pengalaman yang relatif sama dalam menggunakan

Exelsa maka kedua moderator ini tidak digunakan. Hal inilah

yang dapat menyebabkan tidak signifikannya konstruk facilitating conditions dalam mempengaruhi use behavior.

Penelitian ini juga menemukan bahwa behavioral intention

memiliki pengaruh yang positif dan signifikan terhadap use

behavior. Temuan ini sesuai dengan konsep dasar dari model-

model penerimaan pengguna yaitu, niat untuk menggunakan

teknologi informasi akan mempengaruhi penggunaan

sebenarnya teknologi informasi tersebut [20].

VI. KESIMPULAN DAN SARAN

Berdasarkan hasil analisis empirik dan pembahasan yang

telah dilakukan dalam penelitian ini, maka dapat disimpulkan

bahwa (1) Variabel performance expectancy, social influence

dan facilitating conditions terbukti signifikan mempengaruhi

behavioral intention Mahasiswa Universitas Sanata Dharma

dalam menggunakan Exelsa. Sementara variabel effort

expectancy terbukti tidak signifikan. Variabilitas behavioral

intention dapat diterangkan 27,3% dengan menggunakan

variabel performance expectancy, social influence, effort

expectancy dan facilitating conditions. (2) Variabel behavioral intention terbukti signifikan mempengaruhi use behavior.

Sementara variabel facilitating conditions terbukti tidak

signifikan. Variabilitas use behavior dapat diterangkan 5,5%

dengan menggunakan variabel behavioral intention dan

facilitating conditions. (3) Hasil penelitian menunjukkan

bahwa model UTAUT belum bisa menjelaskan dengan baik

penerimaan dan penggunaan Learning Management System di

kalangan mahasiswa Universitas Sanata Dharma.

Agar pemanfaatan Exelsa lebih meningkat, peneliti

menyarankan hal-hal sebagai berikut (1) Cara mengajar

sebaiknya dirancang sedemikian rupa hingga, mahasiswa percaya bahwa penggunaan Exelsa akan secara positif

mempengaruhi performa belajarnya. (2) Para dosen sebaiknya

mendorong mahasiswanya untuk menggunakan Exelsa karena

mahasiswa percaya bahwa orang-orang yang penting baginya,

misalnya dosen menganggap bahwa dia sebaiknya

menggunakan Exelsa. (3) Universitas Sanata Dharma

sebaiknya memberikan dukungan yang memadai terhadap

ketersediaan infrastruktur teknis dan organisasional yang

mendukung penggunaan Exelsa.

UCAPAN TERIMA KASIH

Penulis mengucapkan terima kasih kepada pihak-pihak yang telah membantu sehingga penelitian ini dapat

diselesaikan, terutama kepada Ir. Ig. Aris Dwiatmoko, M.Sc.,

Prof. Diogenes de Souza Bido, dan Dr. Christian M. Ringle

atas diskusinya mengenai metode statistik. Terakhir, namun

tidak mengurangi rasa hormat, kami berterima kasih kepada

Puspaningtyas Sanjoyo Adi, S.T., M.T., PH Prima Rosa, S.Si.,

M.Sc., I Gusti Ketut Puja S.T., M.T., dan Damar Widjaja,

S.T., M.T., atas izinnya sehingga kami dapat mengambil data

untuk penelitian ini.

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I Gusti Nyoman Sedana received his Bachelor

degree from the Informatics Engineering

Department, University of Sanata Dharma,

Indonesia, in 2010. His previous research includes

user acceptance models. He is currently a quality

assurance staff member at the Sigma Cipta Caraka

Corporation.

St. Wisnu Wijaya received his Bachelor degree

from the Electrical Engineering Department,

Gadjah Mada University and his Master of

Informatics degree from the Department of

Informatics, Bandung Institute of Technology

(ITB), Indonesia. His current appointments are:

lecturer of Department of Informatics and

researcher at the Center for Information

Technology Studies, Sanata Dharma University,

Indonesia. Since 2009, he has been an expert staff

for the Institute for Migrant Workers. His research

interest is in the information system discipline,

especially the role of information technology in

social change. He has published numerous papers

in peer-reviewed journals and conference

proceeding. Mr St. Wisnu Wijaya has won

numerous research grants, including research grants

from the Government of Indonesia, Sanata Dharma

University and International Donors.

Vol.2/No.2 (2010) INTERNETWORKING INDONESIA JOURNAL 33

ISSN: 1942-9703 / © 2010 IIJ

Abstract—The Concept Hierarchy in attribute oriented

induction is a powerful tool for saving the knowledge hierarchy

in data, which will be then used to generalize mining rules for

data mining. Database design influences the performance

applications when reading records in database. When building

the database table the allocation of fields will be decided by the

lowest and the highest concept within the concept tree.

Additionally, the number of hierarchy concept levels in a concept

tree will decide the quantity of fields in the table. The number of

record tables will be decided by the number of the lowest

concepts in concept tree. For numeric values the treatment is

different. For efficiency reasons the number of created table

records will instead depend on the amount of concepts at the next

generalization. All the tables from concept trees will become

dimensional tables and the data table will become a fact table.

All these tables in fact create a star schema. The Star schema is

recognized as data warehouse schema for multidimensional

analysis, and will give value add for attribute oriented induction

for multidimensional purposes.

Index Terms—Data Mining, concept hierarchy, attribute

oriented induction, rule, database.

I. INTRODUCTION

HE attribute oriented induction method integrates a

machine learning paradigm – especially learning-from-

examples techniques [3] – with database operations, extracts

generalized rules from an interesting set of data and discovers

high level data regularities [13]. The attribute oriented

induction method has been implemented in a data mining

system prototype called DBMINER [16,17] which was

previously called DBLearn [12,14], and has been tested

successfully against a large relational database.

The attribute oriented induction approach was developed for

learning different kinds of knowledge rules such as

characteristic rules, discrimination or classification rules,

quantitative rules, data evolution regularities [15], qualitative

rules [9], association rules and cluster description rules [13].

Attribute oriented induction has the concept hierarchy as an

advantage, where a concept hierarchy as background

knowledge can be provided by knowledge engineers or

Manuscript received March 17, 2010.

Spits Warnars is a doctoral student at the Department of Computing and

Mathematics, Manchester Metropolitan University, John Dalton Building,

Chester Street, Manchester M15GD United Kingdom. (e-mail:

s.warnars@mmu.ac.uk).

domain experts [8,10,13]. Concepts are ordered in a concept

hierarchy by levels from specific or low level concepts into

general or higher level concepts. Generalization is achieved

by ascending to the next higher level concepts along the paths

of concept hierarchy [11]. The most general concept is the null

description as the most specific concepts correspond to the

specific values of attributes in the database described as ANY.

The concept hierarchy can be balanced or unbalanced, but an

unbalanced hierarchy must then be converted to a balanced

one.

There are 3 Types of concept generalization on concept

hierarchy [6] and in [5] number (2)-(4) as 3 types of rule based

concept hierarchy and number (1) as a non rule based concept

hierarchy. These are:

1) Unconditional concept generalization: the rule associated

with the unconditional IS-A type rules. A concept is

generalized to a higher level concept because of the

subsumption relationship indicated in the concept

hierarchy.

2) Conditional/deductive rule generalization: the rule

associated with a generalization path as a deduction rule

where the type of rules is conditional and can only be applied to generalize a concept if the corresponding

condition can be satisfied. For example, form : A(x) Λ

B(x) C(x) has the meaning that for a tuple x, the

concept(attribute value) A can be generalized to concept

C if condition B can be satisfied by x. Or concept C can

be generalized if it can be satisfied by concept A and B.

3) Computational rule generalization: each rule is

represented by a condition which is value-based and can

be evaluated against an attribute or a tuple or the database

by performing some computation. The truth value of the

condition would then determine whether a concept can be

generalized via the path. 4) Hybrid rule-based concept generalization: a hierarchy can

have paths associated with all the above 3 different types

of rules. It has a powerful representation capability and is

suitable for many kinds of application.

An implementation needs a database for storing records,

while the application needs data mining tools for the user to

read records from database and get the rules in for

presentation. The database can be implemented using any kind

of database management system, such as Microsoft Access,

SQL Server, Oracle, MySQL and many other database

management systems. The application can be implemented

using any kind of language software, such as Java, Active

Star Schema Design for Concept Hierarchy in

Attribute Oriented Induction

Spits Warnars

Manchester Metropolitan University, United Kingdom

T

34 INTERNETWORKING INDONESIA JOURNAL WARNARS

ISSN: 1942-9703 / © 2010 IIJ

Server Pages, Visual Basic and others. The choice of the

database and application will depend on a number of factors,

including on the organizational rules, monetary resources, and

the technology.

In considering databases, one important factor is the

performance of the database system since it influences the

performance of the application as a whole. A good database

design will increase the application performance, while a bad

database design will reduce the application performance. An

additional factor is whether the database is normalized. On

one hand the normalization of a database provides for the best

performance and is useful for transactional processing systems

with input, edit and delete operations. On the other hand, an

unnormalized database will provide the best approach for

online analytical processing where records need only to be

read. Moreover, the number of SQL join operations will also

influence performance as a whole. Typically, more join

operations will need to be made in a normalized database

compared to an unnormalized database. The expert knowledge

in the area of database design contributes towards obtaining

the best design and performance. Additionally, a distributed

database can also be used for handling large distributed

concept hierarchies.

In this paper we transform the concept hierarchy into tables

resulting in a new database design. The concept tree will be

used as data input and the rules will be built based on the

queries [23,26,28].

Using queries to build rules presents an efficient mechanism

for understanding the mined rules ([22,25]). The use of the

threshold as a control over the maximum number of tuples

(within the target class in the final generalized relation) will

no longer be needed. Instead, group by operator in the SQL

Select statement will limit the final result generalization.

Setting different thresholds will generate different

generalized tuples (needed of global picture of induction).

Doing this repeatedly is time-consuming and tedious work

[27]. Instead, all the interesting generalized tuples as a

multiple rule can be generated for the global picture of

induction by using group by operator in the SQL Select

statement.

In converting the concept hierarchy into a database table

and in order to obtain the best database design, there are a

number of questions that need to be answered. These

questions include how to perform the conversion (from the

concept hierarchy into a table), how many tables will be

created and based on which criteria. By answering these

questions, we hope that a standard method will emerge for the

conversion of concept hierarchies into good database designs.

In the first instance the first step will be to explore how to

convert the non-rule based concept hierarchy (as an

unconditional concept hierarchy). Future work will address the

conversion of from rule based concept hierarchy.

In this paper we discuss only the conversion of the non-rule

based concept hierarchy into tables in the star schema design.

The implementation of the current and proposed attribute

oriented induction, including the differentiation between them,

can be found in [29].

II. CONVERTING FROM CONCEPT HIERARCHY INTO TABLE

In order to aid the discussion and provide a link between the

current work with our previous research, we will use the

concept hierarchy based on data examples found in [2,8].

Figure 1 shows an example of a concept hierarchy from

university database.

Burnaby

Victoria

...

Edmonton

...

Bombay

...

Nanjing

...

British Columbia

Alberta

...

India

China

...

Comp

Math

Physic

Biology

...

Literature

Music

History

...

Freshman

Sophomore

Junior

Senior

MA

MS

PhD

0.0-1.99

2.0-2.99

3.0-3.49

3.5-4.0

ANY(major)

ANY(Category)

ANY(Birthplace)

ANY(GPA)

Canada

Foreign

Undergraduate

Graduate

Poor

Average

Good

Excellence

Art

Science

Fig. 1. Example of a Concept hierarchy

Based on concept hierarchy in Figure 1 we will build

concept tree that represents a taxonomy of concepts of the

values in an attribute domain. The concept tree will be built

based on the most general concept which is described as ANY

in concept hierarchy. Based on Figure 1 there are four (4)

most general concepts with ANY, resulting in concept trees

[24]. These are:

1) (science, art) ANY(major)

2) (graduate, undergraduate) ANY(category)

3) (Foreign, Canada) ANY(birthplace)

4) (poor, average, good, excellence) ANY(GPA)

The dotted-line symbol (…) in Figure 1 shows the

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possibility of other concepts. For example, there is the

possibility to extend the width of concept tree by extending

the “Major” beyond “Science” and “Art”. Similarly, the

“Birthplace” is not limited to just only “Canada” and

“Foreign”.

For the database implementation all the concept trees will

be implemented as tables in the database. The concept tree for

“Major” will be implemented as the table entitled

hierarchy_major (see Table 1 and its explanation in Figure 2).

The lowest level concept tree for “Major” in Figure 2 like

comp, math, physics, biology, literature, music and history

become the first field name Major and has varchar data type

with length 15. The next and the last level concept tree for

major in figure 2 like science and art become the next field

name StudyProg and have varchar data type with length 15.

TABLE I

HIERARCHY_MAJOR TABLE

Field Name Type

Major varchar(15)

StudyProg varchar(15)

Field Name Type

Major varchar(15)

StudyProg varchar(15)

Fig. 2. The transformation of the concept tree for Major into hierarchy_major

table

For example, if there are 10 lowest level concepts in the

concept tree “Major” (Figure 2) then 10 records or tuples will

be created in the table hierarchy_major based on field

“Major” as the first field in that table. Each of the records will

fill the next field studyprog based on generalization the lowest

level concept tree major. For example for the record where the

major field is “Computing”, its next field studyprog will be

filled with “Science” because the computing as the lowest

level concept tree major in Figure 2 has a generalization into

Science. Table 2 is the result from the data from the concept

tree “Major” in Figure 2.

TABLE II

RECORDS FOR HIERARCHY_MAJOR TABLE

Major StudyProg

Computing Science

Math Science

Biology Science

Chemistry Science

Statistics Science

Physics Science

Music Art

History Art

Literal Arts Art

Literature Art

As another example, the concept tree for “Category” will be

implemented as table hierarchy_cat (see Table 3 and Figure

3). The lowest level concept tree for “Major” in Figure 3 such

as “Freshman”, “Sophomore”, “Junior”, “Senior”, “MS”,

“MA” and “PhD” become the first field named “Category”

and has varchar data type with length 15. The last level

concept tree for “Category” in Figure 3, such as

“Undergraduate” and “Graduate” becomes the next field name

“Study” and has varchar data type with length 15.

TABLE III

HIERARCHY_CAT TABLE

Field Name Type

Category varchar(15)

Study varchar(15)

Field Name Type

Category varchar(15)

Study varchar(15)

Fig. 3. The transformation of the concept tree for Category into hierarchy_cat

table

In Figure 3 there are seven (7) lowest level concepts from

concept tree “Category”, which will create seven (7) records

or tuples in table hierarchy_cat based on field Category as the

first field in the table. The records filling next field “Study”

will be based on the generalization the lowest level concept

tree “Category”. For example the record where the category

field contains “Freshman” will fill the next field (“Study”)

with “Undergraduate” because the concept “Freshman” as the

lowest level concept tree (in “Category” in Figure 3) has a

generalization into “Undergraduate”. Table 4 shows this based

on the data from concept tree “Category” in Figure 3.

TABLE IV

RECORDS FOR HIERARCHY_CAT TABLE

Category Study

Freshman undergraduate

Sophomore undergraduate

Junior undergraduate

Senior undergraduate

MS graduate

MA graduate

PhD graduate

The concept tree for “Birthplace” will be implemented as

the table hierarchy_birth (see Table 5 and Figure 4). The

lowest level concept tree for birthplace in Figure 4 (such as

Burnaby, Victoria, Edmonton, Bombay and Nanjing) will

become the first field name “Birthplace” and has a varchar

data type with length 15. The next level concept tree for

birthplace in Figure 4 (namely British Columbia, Alberta,

India and China) will become the next field name “City” and

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has varchar data type with length 20. Note that this tree is

different from the previous concept due to the fact that it has

three (3) levels. As such, the next and the last level concept

tree for birthplace in Figure 4 (namely Canada and Foreign)

will become the next field name “Country” and has a varchar

data type with length 10. Thus, the number of hierarchy levels

will determine the number of the fields that will be created. In

the case of the previous concept tree, because there are two (2)

hierarchy levels, therefore two fields will be created for each

concept tree. Similarly, since the concept tree “Birthplace” has

three (3) hierarchy levels, therefore three fields will be created

in the table.

TABLE V

HIERARCHY_BIRTH TABLE

Field Name Type

Birthplace varchar(15)

City varchar(20)

Country varchar(10)

Field Name Type

Birthplace varchar(15)

City varchar(20)

Country varchar(10)

Fig. 4. The transformation of the concept tree for Birthplace into

hierarchy_birth table

Following the previous example, there are eleven (11)

lowest level concepts in the concept tree “Birthplace” as

shown in Figure 4. This will create eleven records or tuples in

table hierarchy_birth based on field “Birthplace” as the first

field in the table. In each record the next field entry will be

based on the generalization on concept tree “Birthplace”.

TABLE VI

RECORDS FOR HIERARCHY_BIRTH TABLE

Birthplace City Country

Bombay India Foreign

Burnaby British Columbia Canada

Calgary Alberta Canada

Edmonton Alberta Canada

Nanjing China Foreign

Ottawa Ontario Canada

Richmond British Columbia Canada

Shanghai China Foreign

Toronto Ontario Canada

Vancouver British Columbia Canada

Victoria British Columbia Canada

For example the record where the birthplace field contains

the value “Bombay” will have the next field (“City”) filled

with the value “India” because the concept “Bombay” as the

lowest level in the concept tree “Birthplace” (see Figure 4) has

a generalization into the India concept. Furthermore, the next

field “Country” (which is the last field) with have the value

“Foreign” because the concept “India” has a generalization

into “Foreign” concept. Table 6 shows the resulting data from

concept tree “Birthplace” as shown in Figure 4.

The concept tree “GPA” will be implemented as table

hierarchy_gpa (see Table 7 and Figure 5). Different to the

previous concept trees, here there are a large range data for

hierarchy levels. For example, the generalization for concept

“Poor” comes from the value range between 0 and 1.99, and

thus there will be 199 values possible (covering from 0.00 to

1.99). For efficiency reasons we just record the first value and

last value in the range for each of hierarchy levels. As result,

we will add one field containing the last value implying that

“The number of hierarchy levels will decide the number of

fields will be created”. We use this notation here because

although the concept tree “GPA” (Figure 5) has only two

levels (and thereby creating two fields in the database), adding

199 records to each field will result in a table with a total of

400 records. Thus for efficiency reasons we treat concept trees

with numeric values differently [11,21,19,20]. Therefore, here

we have created only 3 fields with 4 records.

In the case of the GPA, the first field is GPA_start and will

have a range a values using the float(3,2) data type. Next is

field name GPA_fin that will also have a range a values using

the float(3,2) data type. The last field name “Range” will be

the same as the other concept trees where it has “the highest

level before the most general concept ANY is the last field”.

The field range has varchar data type with length 15.

TABLE VII

HIERARCHY_GPA TABLE

Field Name Type

GPA_start float(3,2)

GPA_fin float(3,2)

range varchar(15)

Fig. 5. The transformation of the concept tree for GPA into hierarchy_gpa

table

Because GPA concept tree in Figure 5 handles numeric

values, then the number of created records will not depend on

the number of concepts at the lowest level in concept tree.

Instead for efficiency it will depend on the number of concepts

at the next generalization. Now at the next generalization

(after the lowest level concept) there are four (4) concepts,

namely “Poor”, “Average”, “Good”, and “Excellent”.

Different to the other cases, the handling of numeric values in

the concept tree will be via specialization. For example the

first data at the level after the first low level is “Poor” and we

assign the GPA_start value of 0 and the GPA_fin with the

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value of 1.99. We do this also for the other fields. The result is

shown in Table 8 using the concept tree in Figure 5.

TABLE VIII

RECORDS FOR HIERARCHY_GPA TABLE

GPA_start GPA_fin range

0.0 1.99 Poor

2.0 2.99 Average

3.0 3.49 Good

3.5 4.0 Excellent

Based on the previous explanation, below are the summary

of the assumptions that we have used to convert a concept

hierarchy into a table:

1) The lowest level concept tree maps to the first field and

the highest level (before the most general concept ANY)

is mapped to the last field.

2) The number of hierarchy levels in a concept tree will

decide the number of fields in the table, with the

exception of numeric values for efficiency reasons.

3) The number of concepts at the lowest level in a concept

tree will become the number of records or tuples in the

table, with the exception of numeric values for efficiency

reasons.

4) For the efficiency handling numeric values in a concept tree, the number of created table records will not depend

on the number of concepts at the lowest level in concept

tree, but instead it will depend on the number of concepts

at the next generalization.

III. LOGICAL DATA MODEL

Based on data examples found in [2,8], Table 9 shows the

corresponding student table.

TABLE IX

STUDENT TABLE

Name Category Major Birthplace GPA

Anton M.A. History Vancouver 3.5

Andi Junior Math Calgary 3.7

Amin Junior Liberal arts Edmonton 2.6

Anil M.S. Physics Ottawa 3.9

Ayin Ph.D. Math Bombay 3.3

Amir Sophomore Chemistry Richmond 2.7

Acai Senior Computing Victoria 3.5

Abdi Ph.D. Biology Shanghai 3.4

Afun Sophomore Music Burnaby 3.0

Agung Ph.D. Computing Victoria 3.8

Ahing M.S. Statistics Nanjing 3.2

Akuan Freshman literature Toronto 3.9

All the tables that represent a concept hierarchy can be

connected to the student table. Figure 6 shows a class diagram

with the connectivity between tables. In the context of the

Data Warehouse concept, Figure 6 shows a star schema,

where the student table is viewed as a fact table and the other

tables from the concept tree are viewed as a dimensional table.

As a result, the multi dimensional concept in Data Warehouse

can be applied here, where the data can be roll up and drill

down and the data can be viewed in multiple dimensions with

concept slice, dice and pivot [4, 18]. The aggregate count

function and group-by-operator in the SQL Select statement

can represent the roll up process [1, 7].

Fig. 6. A Logical data model

To improve the performance the logical data model in

Figure 6, it can be changed to become one (1) table (as only

one fact table) as shown in Table 10. The data as the

representative all the data in Figure 6 is shown in Table 11 and

12. The performance can be improved by using unnormalized

tables. This is because using one table is faster rather than

using 5 tables through the decrease in the number of needed

Join operations. However, the limitation of using only one

fact table is that it poorly represents the concept hierarchy. For

efficiency needs these two options can be used together, where

queries will be directed at the single fact table and the concept

hierarchy is used as the basis for the logical data model as

exemplified by Figure 6.

TABLE X

ONE FACT TABLE

Student

Name

Category

Study

Major

Studyprog

Birthplace

City

Country

GPA

Range

TABLE XI

DATA IN ONE FACT TABLE

Name Category Study Major StudyProg

Anton MA graduate History art

Andi Junior undergraduate Math science

Amin Junior undergraduate Literal Arts art

Anil MS graduate Physics science

Ayin PhD graduate Math science

Amir Sophomore undergraduate Chemistry science

Acai Senior undergraduate Computing science

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Abdi PhD graduate Biology science

Afun Sophomore undergraduate Music art

Agung PhD graduate Computing science

Ahing MS graduate Statistics science

Akuan Freshman undergraduate Literature art

TABLE XII

CONTINUE DATA IN ONE FACT TABLE

Birthplace City Country GPA Range

Vancouver British Columbia Canada 3.5 Excellent

Calgary Alberta Canada 3.7 Excellent

Edmonton Alberta Canada 2.6 Average

Ottawa Ontario Canada 3.9 Excellent

Bombay India Foreign 3.3 Good

Richmond British Columbia Canada 2.7 Average

Victoria British Columbia Canada 3.5 Excellent

Shanghai China Foreign 3.4 Good

Burnaby British Columbia Canada 3 Good

Victoria British Columbia Canada 3.8 Excellent

Nanjing China Foreign 3.2 Good

Toronto Ontario Canada 3.9 Excellent

Another option for efficiency is by the appropriate coding

of the student data which can provide storage space savings,

better query performance and easier data management. The

following is an example of assigning codes to the field entries:

1) Coding for entries in the field studyprog: science 01, art

02.

2) Coding for entries in the field major: computing01,

Math02, Biology03, Chemistry04, statistics05, physics06, Music07, History08, Literal arts09,

Literature 10.

3) Coding for the field Study: undergraduate01,

graduate02.

4) Coding for the field Category: Freshman01,

Sophomore02, Junior03, Senior04, MS05,

MA06, PhD07.

5) Coding for the field Birthplace: Bombay01,

Burnaby02, Calgary03, Edmonton04,

Nanjing05, Ottawa06, Richmond07,

Shanghai08, Toronto09, Vancouver10, Victoria11.

6) Coding for the field City: India01, British

Columbia02, Alberta03, China04, Ontario05.

7) Coding for the field Country: Canada01, foreign02.

Note that the table representing hierarchy_gpa cannot be

coded because of its numeric data. Table 13 shows the coding

result for hierarchy_birth table, Table 14 for the hierarchy_cat

table, Table for the student table, and Table 16 shows the

coding result for hierarchy_major table.

TABLE XIII

CODING FOR HIERARCHY_BIRTH TABLE

BirthID Birthplace City Country

010102 Bombay India Foreign

020201 Burnaby British Columbia Canada

030301 Calgary Alberta Canada

040301 Edmonton Alberta Canada

050402 Nanjing China Foreign

060501 Ottawa Ontario Canada

070201 Richmond British Columbia Canada

080402 Shanghai China Foreign

090501 Toronto Ontario Canada

100201 Vancouver British Columbia Canada

110201 Victoria British Columbia Canada

TABLE XIV

CODING FOR HIERARCHY_CAT TABLE

CatID Category Study

0101 Freshman undergraduate

0102 Sophomore undergraduate

0103 Junior undergraduate

0104 Senior undergraduate

0205 MS graduate

0206 MA graduate

0207 PhD graduate

TABLE XV

CODING FOR STUDENT TABLE

name Category Major Birthplace

Anton 0206 0208 100201

Andi 0103 0102 030301

Amin 0103 0209 040301

Anil 0205 0106 060501

Ayin 0207 0102 010102

Amir 0102 0104 070201

Acai 0101 0101 110201

Abdi 0207 0103 080402

Afun 0102 0207 020201

Agung 0207 0101 110201

Ahing 0205 0105 050402

Akuan 0101 0210 090501

TABLE XVI

CODING FOR HIERARCHY_MAJOR TABLE

MajorID Major StudyProg

0101 Computing Science

0102 Math Science

0103 Biology Science

0104 Chemistry Science

0105 Statistics Science

0106 Physics Science

0207 Music Art

0208 History Art

0209 Literal Arts Art

0210 Literature Art

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IV. CONCLUSION

In order to convert a non-rule based concept hierarchy,

there are some guidance which can help in the conversion.

First, the lowest level concept is mapped to the first field and

the highest level before the most general concept ANY is

mapped to the last field. Secondly, the number of hierarchy

levels will determine the number of fields except for numeric

value (for efficiency reasons). Thirdly, the number of concepts

at the lowest level in concept tree will become the number of

records or tuples in the table (again with the exception of

numeric values for efficiency reasons). Finally, for the

efficiency handling numeric values in a concept tree, the

number of created table records will not depend on the number

of concepts at the lowest level in concept tree, but instead on

the number of concepts at the next generalization. From an

implementation perspective, there is the option of using one

fact table or several combined tables, and there is also the

option of using coding to improve efficiency and performance.

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Spits Warnars received his bachelor‟s degree in

Information System from the University of Budi

Luhur, Indonesia in 1995. He finished a master

degree in Information Technology from university

of Indonesia, in January 2007. Since 1995 he has

been a full time lecturer at the Information

Technology faculty, at University of Budi Luhur, in

Indonesia. He has also been an auxiliary lecturer

since January 2008 at the Bina Nusantara

University in Indonesia. Spits Warnars has been

completing his PhD degree in computer science at

Manchester Metropolitan University, UK, since

Sept 2008. His area of interest is data mining. He

has published 3 papers in international journals, 6

papers at international conferences, 10 papers in

Indonesian journals, and 7 papers at national

conferences in Indonesia. He has been an ACM

student member since 2008. He has been a reviewer

for the World Scientific and Engineering Academy

and Society since 2006. Since July 2010 he has

been on the editorial board of the Journal of

Computing and Applications (JCA), International

Association for Computer Scientists and Engineers

(IACSE) and Journal of Global Research in

Computer Science (JGRCS).

Internetworking Indonesia JournalThe Indonesian Journal of ICT and Internet Development

ISSN: 1942-9703

About the Internetworking Indonesia Journal

The Internetworking Indonesia Journal (IIJ) was established out of the need to address the lack of an Indonesia-wide independent academic and professional journal covering the broad area of Information and Communication Technology (ICT) and Internet development in Indonesia.

The broad aims of the Internetworking Indonesia Journal (IIJ) are therefore as follows:

Provide an Indonesia-wide independent journal on ICT• : The IIJ seeks to be an Indonesia-wide journal independent from any specific institutions in Indonesia (such as universities and government bodies). Currently in Indonesia there are numerous university-issued journals that publish papers only from the respective universities. Often these university journals experience difficulty in maintaining sustainability due to the limited number of internally authored papers. Additionally, most of these university-issued journals do not have an independent review and advisory board, and most do not have referees and reviewers from the international community.

Provide a publishing venue for graduate students• : The IIJ seeks also to be a publishing venue for graduate students (such as Masters/S2 and PhD/S3 students) as well as working academics in the broad field of ICT. This includes graduate students from Indonesian universities and those studying abroad. The IIJ provides an avenue for these students to publish their papers to a journal which is international in its reviewer scope and in its advisory board.

Improve the quality of research & publications on ICT in Indonesia• : One of the long term goals of the IIJ is to promote research on ICT and Internet development in Indonesia, and over time to improve the quality of academic and technical pub-lications from the Indonesian ICT community. Additionally, the IIJ seeks to be the main publication venue for various authors worldwide whose interest include Indonesia and its growing area of information and communication technology.

Provide access to academics and professionals overseas• : The Editorial Advisory Board (EAB) of the IIJ is intentionally com-posed of international academics and professionals, as well as those from Indonesia. The aim here is to provide Indonesian authors with access to international academics and professionals who are aware of and sensitive to the issues facing a develop-ing nation. Similarly, the IIJ seeks to provide readers worldwide with easy access to information regarding ICT and Internet development in Indonesia.

Promote the culture of writing and authorship• : The IIJ seeks to promote the culture of writing and of excellent authorship in Indonesia within the broad area of ICT. It is for this reason that the IIJ is bilingual in that it accepts and publishes papers in English and Bahasa Indonesia. Furthermore, the availability of an Indonesia-wide journal with an international advisory board may provide an incentive for young academics, students and professionals in Indonesia to develop writing skills appropriate for a journal. It is hoped that this in-turn may encourage them to subsequently publish in other international journals in the future.

Focus & Scope: The Internetworking Indonesia Journal (IIJ) aims to become the foremost publication for practitioners, teachers, researchers and policy makers to share their knowledge and experience in the design, development, implementation, and the man-agement of ICT and the Internet in Indonesia.

Topics of Interest: The journal welcomes and strongly encourages submissions based on interdisciplinary approaches focusing on ICT & Internet development and its related aspects in the Indonesian context. These include (but not limited to) information technology, communications technology, computer sciences, electrical engineering, and the broader social studies regarding ICT and Internet development in Indonesia. A list of topics can be found on the journal website at www.InternetworkingIndonesia.org.

Open Access Publication Policy: The Internetworking Indonesia Journal provides open access to all of its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. This follows the philosophy of the Open Journal Systems (see the Public Knowledge Project at pkp.sfu.ca). The journal is published electronically (PDF format) and there are no subscription fees. Such access is associated with increased readership and increased citation of an author’s work.

Internetworking Indonesia JournalISSN: 1942-9703

www.InternetworkingIndonesia.org