Message from the APIEMS President

Greeting and a warm welcome to the participants of the 15th Asia Pacific Industrial Engineering

and Management Systems Conference. Started in 1998, APIEMS has grown to become the premier

conference for industrial engineering and management systems in the region with participants from

all around the world. The main theme of this year conference: “Sustainable Industrial Systems and

Big Data Management”, is an attempt to address the balance among economic and technical devel-

opment, social development, and environmental protection in this fast changing world.

I congratulate and thank Prof. Dr. Chi-Hyuck Jun, the conference chair, whose leadership made this

APIEMS 2014 conference possible. We are also grateful for the enthusiastic support of APIEMS

from the KIIE and the Korea research community.

On behave of the Asia Pacific Industrial Engineering and Management Society, I wish you a suc-

cessful conference with many thoughtful discussions and debates with old and new friends.

Professor Voratas Kachitvichyanukul

APIEMS President, (2013-2014)

Professor of Industrial & Manufacturing Engineering

Dean, School of Engineering and Technology

Asian Institute of Technology, THAILAND

Message from the General Chair

Welcome to APIEMS 2014 in Jeju City, a beautiful island located at the most south of Korea. It is

our great pleasure to organize this conference, which is supported by Korean Institute of Industrial

Engineers (KIIE). APIEMS conferences have rapidly emerged as an important forum for exchange

of ideas and information about latest developments in the field of industrial engineering and man-

agement systems among professionals mostly from Asia-Pacific countries. APIEMS 2014 confer-

ence encourages contributors to address the topical theme: Sustainable Industrial Systems and Big

Data Management. Papers will represent the latest academic thinking and successful case examples.

The wider audience will benefit from the knowledge and experience of leading practitioners and

academics in this area.

The conference seeks research contributions from researchers, educators, modelers, software devel-

opers, users and practitioners. We hope that you enjoy participating in APIEMS 2014 and staying

in Jeju.

Professor Chi-Hyuck Jun

General Chair, APIEMS 2014

Industrial & Management Engineering

POSTECH, Korea

Conference Committee Members

Conference Committee

• Conference Chair• Chi-Hyuck Jun (POSTECH, Korea)

• Honorary Chairs• Hark Hwang (KAIST, Korea)• Mooyoung Jung (UNIST, Korea)• Kap Hwan Kim (Pusan National Univ., Korea; President, KIIE)

• Conference Co-Chairs (International Advisory Board)• Abdul Hakim Halim (InstitutTeknologi Bandung, Indonesia)• Anthony Shun Fung Chiu (De La Salle University, Philippines)• Baoding Liu (Tsinghua University, China)• Bernard Jiang (National Taiwan University of Science and Technology, Taiwan)• C. J. Liao (National Taiwan University of Science and Technology, Taiwan)• Che-Fu Chien (National Tsing Hua University, Taiwan)• Du-Ming Tsai (Yuan Ze University, Taiwan)• ErhanKozan (Queensland University of Technology, Australia)• HirokazuKono (Keio University, Japan)• Jin Peng (Huanggang Normal University, China)• Jinwoo, Park (Seoul National Univ., Korea)• Katsuhiko Takahashi ( Hiroshima University, Japan)• Kazuyoshi Ishii (Kanazawa Institute of Technology, Japan)• Kin Keung Lai (City University of Hong Kong, Hong Kong)• Mao Jiun Wang (National Tsing Hua Univeristy, Taiwan)• Min K. Chung (POSTECH, Korea)• Mitsuo Gen (Fuzzy Logic Systems Institute, Japan)• P. L. Chang (Feng Chia Uni)• Shouyang Wan (Chinese Academy of Sciences, China)• Tae Eog Lee (KAIST, Korea)• Takashi Oyabu (Kanazawa Seiryo University, Japan)• VoratasKachitvichyanukul (Asian Institute of Technology, Thailand)

• Yon-Chun Chou (National Taiwan University, Taiwan)• Young Hae Lee (Hanyang University, Korea)• ZahariTaha (Universiti Malaysia Pahang, Malaysia)

Organizing Committee

• Technical Program Chairs• Il-Kyeong Moon (Seoul National Univ., Korea)• Byung-In Kim (POSTECH, Korea)

• Publication Chairs• Jaewook Lee (Seoul National Univ., Korea)• Hosang Jung (Inha Univ., Korea)

• Publicity Chairs• Chulung Lee (Korea Univ., Korea)• Yoo-Suk Hong (Seoul National Univ., Korea)

• Sponsorship Chairs• Minseok Song (UNIST, Korea)• Young Jin Kim (Pukyong National Univ., Korea)

• Exhibition Chairs• Hyunbo Cho (POSTECH, Korea)• Yonghui Oh (Daejin Univ., Korea)

• Finance Chair• Dong-Ho Lee (Hanyang Univ., Korea)

• Award Chairs• Kyung sik Lee (Seoul National Univ., Korea)• Young Jae Jang (KAIST, Korea)

• Local Arrangement Chair• Dong-Cheol Lee (Jeju National Univ., Korea)

Conference Sponsors

The Korean Federation of Science and Technology Societies

DOOSAN

SAS KOREA

Pohang University of Science and Technology

The Korean Operations Research and Management Science Society

THE KOREAN OPERATIONS RESEARHAND MANAGEMENT SCIENCE SOCIETY

Keynote Speech

Keynote Speech I Research Issues in Future Logistics

Oct 13 (Monday) 11:00-12:00

Room: Ramada-1

Chung– Yee LeeHong Kong University of Science and Technology, China

Dr. Chung-Yee Lee is Chair Professor/Cheong Ying Chan Professor of Engineering in the Depart-

ment of Industrial Engineering & Logistics Management at Hong Kong University of Science and

Technology. He served as Department Head for seven years (2001- 2008). He is also the Founding

and Current Director of Logistics and Supply Chain Management Institute. He is a Fellow of the

Institute of Industrial Engineers in U.S. and also a Fellow of Hong Kong Academy of Engineering

Science. Before joining HKUST in 2001, he was Rockwell Chair Professor in the Department of

Industrial Engineering at Texas A&M University. He worked as a plant manager and also had few

years consulting experience in Taiwan. In the past thirty years he has engaged in more than forty

research projects sponsored by NSF, RGC, ITF, IBM, Motorola, AT&T Paradyne, Harris Semicon

ductor, Northern Telecom, Martin Marietta, Hong Kong Air Cargo Terminal, Hongkong Interna-

tional Terminal, Philips Medical, ...,etc.

His search areas are in logistics and supply chain management, scheduling and inventory manage-

ment. He has published more than 130 papers in refereed journals. According to an article in Int. J.

Prod. Eco. (2009), which looked at all papers published in the 20 core journals during last 50 years

in the field of production and operations management, he was ranked No. 6 among all researchers

worldwide in h-index.

He received a BS degree in Electronic Engineering (1972) and a MS degree in Management Sci-

ences (1976) both from National Chiao-Tung University in Taiwan. He also received a MS degree

in Industrial Engineering from Northwestern University (1980) and PhD degree in Operations Re-

search from Yale University (1984).

Keynote Speech

Keynote Speech II Data-Driven Decision Making in Manufacturing:Lessons Learned and Future Opportunities

Oct 14 (Tuesday) 11:00-12:00

Room: Ramada-1

Ronald G. Askin Arizona State University, USA

Ronald G. Askin, Ph.D., is a Professor of Industrial Engineering and Director of the School of

Computing, Informatics, and Decision Systems Engineering at Arizona State University. Professor

Askin received his B. S. in Industrial Engineering from Lehigh University followed by an M.S. in

Operations Research and PhD in Industrial and Systems Engineering from the Georgia Institute of

Technology. He has over 30 years of experience in the development, teaching and application of

methods for systems design and analysis with particular emphasis on production and material flow

systems. Other interests include quality engineering and decision analysis. He has published over

120 journal and conference proceedings papers in these areas.

Dr. Askin is a Fellow of the Institute of Industrial Engineers (IIE) and serves as Editor-in-Chief

of IIE Transactions. He has served on the IIE Board of Trustees, as President of the IIE Council

of Fellows, Chair of the Association of Chairs of Operations Research Departments (ACORD)

Chair of the Industrial Engineering Academic Department Heads (CIEADH) and President of the

INFORMS Manufacturing and Service Operations Management Society (MSOM). He was also

General Chair of the 2012 INFORMS Annual Conference. His list of awards includes a National

Science Foundation Presidential Young Investigator Award, the Shingo Prize for Excellence in

Manufacturing Research, IIE Joint Publishers Book of the Year Award (twice), IIE Transactions on

Design and Manufacturing Best Paper Award (twice), the Eugene L. Grant best paper award from

The Engineering Economist, and the IIE Transactions Development and Applications Award.

Keynote Speech

Keynote Speech III Big Data Management

Oct 14 (Tuesday) 13:00-14:00

Room: Ramada-1

Sungzoon ChoSeoul National University, Korea.

Sungzoon Cho is currently professor of Industrial Engineering Department, the director of Data

Mining Center at Seoul National University (SNU) and a member of Government 3.0 Committee

of Korean government. He is on the editorial board of International Journal of Operations Research

and Information Systems and International Journal of Cognitive Biometrics. He served as the presi

yundai Motors, Hyundai Heavy Industries, POSCO, Daewoo Shipbuilding and Marine Engineer-

ing, LG Electronics, Doosan Infracore, SK Hynix, SK Telecommunication and CJ. He advised nine

PhDs and 56 Master students. He teaches Data Mining and Computational Intelligence at SNU as

well as at firms. He received BS and MS in Industrial Engineering at SNU. He won a Fulbright

Scholarship to obtain Masters and PhD at University of Washington in Seattle, US, and University

of Maryland in College Park, US, respectively.

Conference at a Glance

Oct 12 (Sunday) Oct 13 (Monday) Oct 14 (Tuesday) Oct 15 (Wednesday)

08:00-17:00 Registration08:00-17:00 Registration

08:00-12:00 Registration

08:30-10:10 Technical sessionsMA 08:30-10:10 Technical sessions

WA

08:40-10:40 Technical sessions TA

10:00-18:00 Registration

10:10-10:30 Coffee break 10:10-10:30 Coffee break

10:30-11:00

Opening addresses :APIEMS President,

KIIE President,General Chair

10:30-12:10 Technical sessionsWB

10:40-11:00 Coffee break

11:00-12:00

Keynote speech I(Prof. Chung-Yee Lee:

Research issues in Future Logistics)

11:00:12:00

Keynote speech II(Prof. Ronald Askin:

Data-Driven Decision Making in

Manufacturing)

13:00-17:20 Excursion

12:00-13:30 Lunch 12:00-13:00 Lunch 12:10-13:30 Lunch

13:30-15:30 Technical sessionsMB

13:00-14:00

Keynote speech III(Prof. Sungzoon Cho:

Big DataManagement)

14:00-14:20 Coffee break

14:20-16:00 Technical sessionsTB

15:30-15:50 Coffee break

15:50-17:50 Technical sessionsMC

16:00-16:20 Coffee break

Registration16:20-18:00 Technical sessions

TC

13:00-18:00 Poster Session

18:00-20:00 WelcomeReception 18:30-21:00 General Reception

Oct 12 (Sunday)10:00-18:00 Registration13:00-17:20 Excursion18:00-20:00 Welcome Reception

Oct 13 (Monday)08:00-17:00 Registration

Room Mara Biyang Udo Chuja Ramada-1 Ramada-2 Ramada-3 Ramada-4 Halla(8F)

08:30-10:10Technical sessions MA

MA1 MA2 MA3 MA4 MA5 MA6 MA7 MA8 MA9

Sessionname Data Mining 1

Management of Technology

andInnovations 1

ERP/E-Business

ServiceSciences 1

QualityEngineering

&Management 1

Production andOperations

Management 1Metaheuristics

Financial Models &

Engineering

Uncertainty Theory (Ses-

sion I)

Paper #

528 100 37 54 23 75 42 41 551207 111 38 55 28 158 43 146 555276 143 352 108 109 211 175 180 556324 44 360 215 113 269 353 267 584296 97 255 244 226 213 465 273

10:10-10:30 Coffee break10:30-11:00 Opening addresses: APIEMS President, KIIE President, General Chair

11:00-12:00 Keynote speech I (Prof. Chung-Yee Lee: Research Issues in Future Logistics)

12:00-13:30 Lunch

13:30-15:30Technical sessions MB

MB1 MB2 MB3 MB4 MB5 MB6 MB7 MB8 MB9

Sessionname

Decision Sup-port Systems

& Expert Systems

Probability & Statistical

Modeling

Ergonomics/Human

Factors 1Service

Sciences 2

QualityEngineering

&Managment 2

Production and

Operations Management 2

GreenManufacturing/Management

TransportationErgonomics & Welfare Man-

agement

Paper #

173 190 96 322 227 338 417 73 488254 299 131 401 228 362 550 91 484290 333 305 411 229 394 119 103 530460 334 315 479 346 396 156 312 485116 3354 326 504 294 442 342 340 471538 450 332 323 307 361 53 505

15:30-15:50 Coffee break

15:50-17:50Technical sessions MC

MC1 MC2 MC3 MC4 MC5 MC6 MC7 MC8 MC9

Sessionname

Supply Chain Management 1

Reliability & Maintenance

Ergonomics/Human

Factors 2Network

Optimization

QualityEngineering

&Management 3

Simulation 1 Healthcare Systems 1

Optimization Techniques 1

Educational SupportSystem

Paper #

252 118 456 407 325 500 482 374 501261 121 359 363 328 196 99 217 562279 153 393 268 339 424 112 201 448280 320 419 515 346 66 194 169 455355 580 449 319 370 179 248 206 154336 582 341 142 402 271 507

Oct 14 (Tuesday)08:00-17:00 Registration

Room Mara Biyang Udo Chuja Ramada-1 Ramada-2 Ramada-3 Ramada-4 Halla(8F)

08:40-10:40Technical sessions TA

TA1 TA2 TA3 TA4 TA5 TA6 TA7 TA8 TA9

Sessionname

Supply Chain Management 2

Communication Support Data Mining 2

Tourism Management/

Topics inIE/MS

Sustainable Management Simulation 2

Production & Operations

Management 1Logistics

ManagementUncertainty

Theory(Session II)

Paper #

50 443 128 472 35 98 282 440 55859 535 147 444 114 105 327 477 55960 489 203 564 136 221 349 483 56061 536 392 15 137 272 431 543 561

130 480 412 264 291 295 104 344 565161 537 216 225 347 356 218 313 428

10:40-11:00 Coffee break11:00-12:00 Keynote speech II (Prof. Ronald Askin: Data Driven Decision Making in Manufacturing)

12:00-13:00 Lunch13:00-14:00 Keynote speech III (Prof. Sungzoon Cho: Big Data Management)

14:00-14:20 Coffee break

14:20-16:00Technical sessions TB

TB1 TB2 TB3 TB4 TB5 TB6 TB7 TB8 TB9

Sessionname

Supply Chain Management 3

Management of Technology

andInnovations 2

Data Mining 3 Scheduling & Sequencing 1

Knowledge & Information

Management

Production & Operations

Management 2Healthcare Systems 2

Flexible Manufacturing

SystemsTopics in IE/MS

Paper #

165 188 437 122 250 49 95 579 575176 425 469 233 278 124 106 48 354208 317 486 284 445 151 306 62 378160 150 502 287 297 187 379 286 212234 22 581 309 389 12 76 457 202

16:00-16:20 Coffee break

16:20-18:00Technical sessions TC

TC1 TC2 TC3 TC4 TC9

Sessionname

Heuristics/Me-taheuristics

Inventory Mod-eling / Artificial

IntelligenceArtificial Intel-

ligenceScheduling & Sequencing 2

Lean Produc-tion Manage-

ment

Paper #

70 381 182 399 542464 123 260 405 546481 101 490 418 94520 318 391 398 545192 499 79 547

13:00-18:00 POSTER Session

Paper #47 149 166 204 220 245 253 265 205

365 366 382 400 414 422 432 435 524451 473 487 522 527 491 420 145

18:30-21:00 General Reception

Oct 15 (Wednesday)08:00-12:00 Registration

Room Mara Biyang Udo Chuja Ramada-3 Ramada-4 Ramada-1 Ramada-2

08:30-10:10Technical sessions WA

WA1 WA2 WA3 WA4 WA5 WA6

Sessionname

Inventory Mod-eling & Manage-

mentSCM and

Forecasting 1Production Design &

Management 1Scheduling & Sequencing 3 Fuzzy Logic Optimization

Techniques 2

Paper #

65 92 117 85 30 12580 31 162 120 58 6971 34 198 177 224 288

446 32 222 316 576 577518 102 249 509 415

10:10-10:30 Coffee break

10:30-12:10Technical sessions TB

WB1 WB2 WB3 WB4 WB5 WB6

Sessionname

Industrial Engineering Education

SCM and Fore-casting 2

Production Design &

Management 2Scheduling & Sequencing 4

Quality Engineering &

ReliabilityLean

Manufacturing

Paper #

526 52 283 329 453 129139 36 348 46 508 371256 87 350 403 270 553495 413 93 426 517 110

84 454 421 516

12:10-13:30 Lunch

Floor Plan

8F

Tamna Hall

OraHall

AraHall

Halla Hall

TechnicalSession(10/13~14)

Ballroom Lobby

2F

RegistrationTechnicalSession

Ramada Ballroom −> BanquetRamada 2,3,4 −> Welcome ReceptionRamada 1,2,3,4 −> Technical Session

RamadaBallroom

Mara Hall

Biyang Hall Chuja Hall

Udo Hall

Poster Session

Proceedings of the Asia Pacific Industrial Engineering & Management Systems Conference 2014

1

Analysis and Proposal about the Effect of Time, Types of

Subject and Types of Room Factor

to the Students’ Concentration

Elty Sarvia

Department of Industrial Engineering

Maranatha Christian University, Bandung, Indonesia

Tel: (+62) 22-2012186 ext 1262/1276, Email : eltysarvia@yahoo.com

Evan Pratama Sentosa

Department of Industrial Engineering

Maranatha Christian University, Bandung, Indonesia

Tel: (+62) 22-2012186 ext 1262/1276, Email: evan_sentosa@yahoo.com

Abstract. Decreasing of the learning concentration was defined as a decreasing ability to concentrate on

learning activity which was reflected through one's behavior (Ahmadi Abu, 2003). This condition affects a

person's understanding. This study aimed to analyze the effect of time, types of subject and types of room

factor to the decrease of students’ concentration in learning and analyze the maximum point of the students to

concentrate in learning and propose ergonomic systems (GWM H02C05 room and H02A07 room,

Department of Industrial Engineering, Maranatha Christian University, Bandung).

Data that were collected in this study were Visual Analogue Scale, Group Bourdon Test and field observations

with 48 total respondents. The further observations were processed using ANOVA test with between-subjects

design (3-ways interaction)

ANOVA test results showed that the time factor and the types of subject factor affected to the learning

concentration of students. Types of room factor did not affect to the learning concentration of students. The

result of Visual Analogue Scale, Group Bourdon Test and observations gave the same result, that learning

concentration of the students was decreased. The proposals that could be given were doing a good course

scheduling such as mathematical subject should be placed in the morning time (at 07.00 am - 11.00 am) and

theoretical subjects placed on the day time (at 11.00 am - 03:00 pm).

Keywords: time, types of subject, types of room factor, VAS, Group Bourdon Test

1. INTRODUCTION

If the decrease of the learning concentration was

further reviewed, it would lead to misunderstanding and

ignorance about the learning materials, which was

essentially a student must know and understand the

learning material provided by an institution, so that there

will be a change in behavior in the learning process that

exist (Moh. Surya, 1977). Thus, it could be said that the

level of understanding in learning was affected by the

learning concentration. If there was a decrease in the

learning concentration, then there was a decrease in the

ability to concentrate on learning activities (Ahmadi Abu,

2003). This condition was reflected from each of the

behavior which is an indicator of a persons’ psychological.

The decrease of the students’ learning concentration was

affected by various factors, including the time, type of

subject and type of room factor.

Researchers determined the initial hypothesis based on

the results of preliminary processing of the data

questionnaire that had been distributed by the researchers to

the students and also the results of the interviews conducted

by researchers introduction. Thus, the following hypothesis

were proposed:

724

Sarvia and Sentosa

1. H1A : There was an effect for students’ learning

concentration from time factor (Factor A).

2. H1B : There was an effect for students’ learning

concentration from type of subject factor (Factor B).

3. H1C : There was an effect for students’ learning

concentration from type of room factor (Factor C).

4. H1AB : There was an effect for students’ learning

concentration from the interaction between the time

factor and type of subject (AB Factor Interactions).

5. H1AC : There was an effect for students’ learning

concentration from the interaction between the time

factor and type of room factor (AC Factor

Interactions)

6. H1BC : There was an effect for students’ learning

concentration from the interaction between the type

of subject and type of room factor (BC Factor

Interactions)

7. H1abc : There was an effect for students’ learning

concentration from the interaction between time

factor, type of subject factor and type of room

factor (ABC Factor Interactions)

8. H 1 : Maximum point (how long (in hours) a student

would be able to concentrate) students’

concentration on learning was set as 1 hour from the

beginning of learning process.

The limitations of this study were as follows :

Participants who became the object of research were

the student of Industrial Engineering Department,

Faculty of Engineering, Maranatha Christian

University.

The total number of respondents would be observed

in this study were 6 respondents for each interaction,

which the total of the interactions were 8.

The independent variable was only based on the time

of factor, type of subject and type of room factor to

know a decrease in the concentration of student

learning. Other independent variables such as age,

gender, consumption and health conditions, physical

work environment, the level of understanding and

ability of students, lecturers way of explanation and

exposure, psychological receiver and so on, did not

discussed in this study.

2. RESEARCH METHOD

The independent variables used by researchers in the

study are:

The time factor (Factor A), which consists of two

levels as before lunch and after lunch conditions.

Type of subject factor (Factor B) which consists of

two levels as mathematical and theoretical subjects.

Type of room factor (Factor C) which consists of

two levels as H02C05 and H02A07 room (Graha

Widya Maranatha).

Preliminary StudyPreliminary questionnaire

Interview with students

Preliminary Data ProcessingTabulation of the results of the preliminary questionnaire

Data Collecting1. Visual Analogue Scale (VAS)

2. Bourdon Group Test

3. Key Behaviour Weight

The Limitations of Study Participants who became the object of research were

the student of Industrial Engineering Department,

Faculty of Engineering, Maranatha Christian

University.

The total number of respondents would be observed

in this study were 6 respondents for each interaction,

which the total of the interaction were 8 .

The independent variables was only based on the

time of factor, type of subject and type of room factor

to know a decrease in the concentration of student

learning.

Research Goal Identify and analyze the effect of time, type of

subject and type of room to decrease of students’

learning concentration.

Identify and analyze the maximum point (hours) of

student would be able to concentrate on learning

process.

Propose an ergonomic system in order to enhance

student learning in terms of the concentration of the

factors that affect the decrease of the students’

learning concentration .

7 Null Hyphotesis Research

Data Processing1. Testing Assumption of ANOVA

2. ANOVA test3. Descriptive Statistics test

Discussion

Conclusion dan Suggestion

Figure 1. Research Framework

725

Sarvia and Sentosa

Table 1. Key Behavior

Figure 2. Data Collecting Scheme

3. DATA COLLECTION

Data collecting for the Visual Analogue Scale (VAS)

was a data collecting carried out by the researcher to

obtained students’ concentration conditions in a

subjectively manner because measuring the perceived level

of concentration of an individual at the time.

Visual Analogue Scale (VAS) is a measurement

instrument that tries to measure a characteristic or attitude

that is believed to range across a continuum of values and

cannot easily be directly measured. For example, the

amount of pain that a patient feels ranges across a

continuum from none to an extreme amount of pain.

Operationally a VAS is usually a horizontal line, 100 mm in

length, anchored by word descriptors at each end, as

illustrated in Figure 3. The VAS score is determined by

measuring in millimetres from the left hand end of the line

to the point that the patient marks. The visual analogue

scale (VAS) has been reported to be the most standardized,

valid and easy to comprehend self-report pain assessment

instrument. (Gould et al, 2002).

Group Bourdon Test is a train driver concentration test.

It is also knows as dot cancellation test. This test based

train driver psychometric used to maintain vigilance, speed,

accuracy, and concentration while looking a group of 4 dots.

Data collection for Group Bourdon Test is a data

collection conducted by researchers to obtain students’

concentration condition in a objectively manner, by

measuring objectively and calculating mathematically

about one’s concentration level.

Data Collecting in a subjectively-objectively manner

by :

a. Measurement of the respondents conducted by the

makers of observation data through behavior of the

respondents (subjective). Weighting on the indicator of

this research conducted individually by each

1 Eyes looked at the left side or right side (turning to the left or right)

2 Eyes looked at downward (head down or asleep)

3 Blank stare (eyes) or daydreaming

1 Pay attention to other things (attention to others conversation or to outside of classroom)

2 Concentration focused to an object

3 VERBAL RESPONSE 1 Did not give a response (question) as oral speech (verbal response) from lecturer

4 DISCLAIMS OR COMPARE - -

5 ANSWER 1 Answering questions negatively (deviate from the problem) or doubtful (uncertain)

6 REPRESENTATION (STATEMENT) 1 Not responding when lecturer asked to respond

1 The position of the body which indicated unpreparedness in learning

2 Yawning

3 Conduct activities outside the classroom that does not mean

4 Rubbing eyes (sleepy)

5 Blinking eyes very often

6 Did not give a response (movement) as a psychomotor response from lecturer

7 No meaning hand gestures

8 EXPRESSIVE RESPONSES 1 Did not have motivation to listen to the lecturer

1 FOCUS VIEWS

2 ATTENTION CONCENTRATION

7 PSYCHOMOTOR RESPONSE

Initial

Visual

Analogue

Scale (VAS)

Initial

Group

Bourdon Test

Field

Observation

Final

Visual

Analogue

Scale (VAS)

Final

Group Bourdon

Test

Key

Behavior

Weight

Before

Treatment

During

Treatment

Post

Treatment

Key

Behavior Researchers’

benchmark for

Observation

726

Sarvia and Sentosa

Figure 3. Visual Analogue Scale (VAS)

Figure 4. Group Bourdon Test

respondent due to the weight of one with the other

respondents will create different results.

b. Measurement of behavior of the respondents through

the key behavior (objective) shown in table 1.

Figure 2 illustrates a data collection scheme conducted by

researchers of the 48 respondents :

Before Treatment : Data collection was performed

outside the classroom before the lecture begins by

using initial Visual Analogue Scale (VAS) and initial

Group Bourdon Test.

During Treatment : Data collection was performed by

observations in the classroom. Initial benchmark of

this observation is the key behavior that have been

described previously (Table 1)

Post Treatment : Data collection was performed

outside the classroom after the lecture is finished by

using the Final Visual Analogue Scale (VAS), Final

Group Bourdon Test and weights of key behavior.

4. RESULT AND DISCUSSION

The overall condition of the concentration of

respondents (using the Visual Analogue Scale: subjective)

before treatment was higher than the post treated condition

as shown in figure 5. The overall condition of the

concentration of respondents (using the Group Bourdon

Test : objective) before treatment was higher than the post-

treated conditions as shown in figure 6. Table 2 showed the

results of the data collection which were performed by the

researchers could be concluded as an eligible data for

ANOVA test (the data is independent, normal distribution

and homogeneous). Table 3 showed the results of the

ANOVA test (used by researchers to answer the initial

research hypothesis 1 to hypothesis 7), it could be

concluded that there are only 2 factors that affected student

learning decreased concentration i.e. the time factor and

interaction between time and type of subject factor using

0.05.

This research found that from the three methods, i.e

Visual Analogue Scale (VAS) ratings, Group Bourdon Test

and ANOVA test, all had the same conclusion (Table 4).

The conclusion was there was an effect for students’

concentration (there was a significant decrease from

students’ learning concentration prior student learning

activities in the classroom to the students’ learning

concentration after learning activities in the classroom).

Descriptive statistics of test results (used by

researchers to answer the initial research hypothesis 8), it

showed that the maximum point required for students to

concentrate is between 0,750 first hours to 1,139 first hours

of their learning process, with a standard deviation 0,178

hours up to 0.643 hours.

So it could be concluded that the maximum point for

the students’ learning concentration required was

approximately 1 hour starting from the beginning of the

first lecture as shown in figure 7.

From the data processing and analysis result, therefore

it was suggested an ergonomic system to enhance the

student’s learning concentration as follow:

a. Allocating particular subjects on certain period

within student’s class time table such as

mathematical subjects should be placed in the

morning time (7.00 am – 11.00 am) and theoretical

subjects placed on the day time (11.00 am- 3.00

pm).

b. Notice the condition of the maximum point of

students in learning, approximately the first 1

hour lecture. Lecturer should be able to regain

students’ concentration by setting their tone up and

down during the lecture or designing games for

the lecture so that students are not bored or sleepy.

c. Changing the 3-credits-course (2 hours 30 minutes)

which only held in one class meeting, became two

classes meeting. (1 hour 40 minutes at the first

class meeting and 50 minutes at the second class

meeting).

d. Hence, for the 2-credits-course (1 hour 40 minutes)

would remain as it is, according to in accordance

with the conditions of the initial conditions of the

Industrial Engineering Department, Maranatha

Christian University.

727

Sarvia and Sentosa

Table 2. Testing Assumption of Anova

Table 3. Result of Anova Test with between-subject design

Figure 5: Visual Analogue Scale (VAS)

Figure 6: Group Bourdon Test

Independence test Durbin-Watson Comparison Decision Conclusion

1,5 - (1,525) - 2,5

Accept Null hyphotesis

Normality test Shapiro-Wilk Comparison Decision Conclusion

(0,084) > 0,05

Accept Null hyphotesis

Homogeneity test Levene Test Comparison Decision Conclusion

(0,221) > 0,05

Accept Null hyphotesis

(0,198) > 0,05

Accept Null hyphotesis

(0,191) > 0,05

Accept Null hyphotesis

Time, type of subject and type of room

factor 1,525 1,5 - 2,5

There are no differences

between the populations

Time factor 0,221 0,05 Variables are homogeneous

Time, type of subject and type of room

factor 0,084 0,05 Normal distribution

Type of sucject factor 0,198 0,05 Variables are homogeneous

Type of room factor 0,191 0,05 Variables are homogeneous

Interaction Source of Variation F ANOVA Decision Conclusion

df1 = 1

df2 = 40

α = 0,05 Reject null hyphotesis

df1 = 1

df2 = 40

α = 0,05 Accept null hyphotesis

df1 = 1

df2 = 40

α = 0,05 Accept null hyphotesis

df1 = 1

df2 = 40

α = 0,05 Reject null hyphotesis

df1 = 1

df2 = 40

α = 0,05 Accept null hyphotesis

df1 = 1

df2 = 40

α = 0,05 Accept null hyphotesis

df1 = 1

df2 = 40

α = 0,05 Accept null hyphotesis

There was an effect from time factor for

student learning concentration

F Table

Time factor (Factor A) 7,328 4,087,328 > 4,08

Type of room factor

(Factor C)1,312 4,08

1,312 < 4,08 There was no effect from type of room factor

for student learning concentration

Type of subject factor

(Factor B)0,098 4,08

0,098 < 4,08 There was no effect from type of subject factor

for student learning concentration

There was no effect between type of subject

and type of room factor for student learning

concentration

Interaction between time and

type of subject factor

(Factor AB)

24,976 4,0824,976 > 4,08 There was an effect between time and type of

subject factor for student learning concentration

1,832 < 4,08There was no effect between time, type of

subject and type of room factor for student

learning concentration

1

2

3

4

5

Interaction between time and

type of room factor (Factor

AC)

0,271 4,080,271 < 4,08 There was no effect between time and type of

room factor for student learning concentration

Interaction between type of

subject and type of room

factor (Factor BC)

0,173 4,080,173 < 4,08

6

7

Interaction between time, type

of subject and type of room

factor (Factor ABC)

1,832 4,08

728

Sarvia and Sentosa

Figure 7 : Maximum Point of the Students’ Learning Concentration (hours)

\

Table 4. Analysis of Three Methods

5. CONCLUSION

From Visual Analogue Scale graphic and Group

Bourdon Test graphic, there was a significant decrease from

students’ learning concentration prior student learning

activities in the classroom to the students’ learning

concentration after learning activities in the classroom.

Based on Anova Testing and analysis result, it was found

the conclusion that there were 2 factors that affected the

students’ learning concentration decrease, which was a

factor of time (Factor A) and the interaction between the

time factor and the type of subject factor (AB Factor

Interactions). Based on descriptive statistics analysis,

students were still able to concentrate on studying for 1

hour (maximum 1,139 hours) in accordance with the initial

hypothesis of the study).

The recommendations that were given to the

Department of Industrial Engineering, Faculty of

Engineering, Maranatha Christian University, Bandung,

Indonesia such as allocating particular subjects on certain

period within student’s class time table; Lecturer should be

able to regain students’ concentration by setting their tone

up and down during the lecture or designing games for the

lecture so that students are not bored or sleepy; Changing

the 3-credits-course became two classes meeting.

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