customer relationship management for determining of sales strategy using association rules mining...
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CUSTOMER RELATIONSHIP MANAGEMENT
FOR DETERMINING OF SALES STRATEGY
USING ASSOCIATION RULES MINING TECHNIQUE, RFM ANALYSIS,AND UML
TECHNIQUE
TRIYODA ARRAHMANDepartment of Agroindustrial Technology,
Faculty Agricultural Technology, Bogor Agricultural University, Bogor, West Java.
Counselor:Dr. Eng. Taufik Djatna, S.TP, M. Si.
OVERVIEW
Highlight Research
Introduction
Purposes
Related Work
Methods
Discussion
Conclusion
HIGHLIGHT RESEARCHBusiness
Competition is The Most
Important Factor
CRM Includes Cross selling as Solution For More Effective
and Efficient Marketing System
Sales Strategy is Made by Analysis of
Transaction Data using Associative
Rules Mining Technique
Sales Strategy Created From
Support, Confidence, and
Improvement Scores
Results: Optimization of
Cross Selling Strategy for Agroindustry
Keywords: CRM (Customer Relationship Management), Sales Strategy, Data Mining, Associative Rules Mining, Cross Selling
Strategy , RFM Analysis
INTRODUCTION
Customer Relationship Management: Integration of Sales Strategy, Marketing, And Service
CRM : Identify Customers In More Detail And Serve Them According To Their Needs
CRM Applications With Cross-Selling Must Be Preceded By An In-depth Analysis Using Data Mining
In This Work We Evaluate A Marketing Strategy Through a Hypothetical Cross-Selling For Optimization CRM
PURPOSES
To Get the Support Score Of Association Rules to Know Size of Domination Level Of
Itemset From All Transaction
To Get the Confidence Score Of Association Rule To Know Size of
Relation Between Two Item by Conditional
To Get the Improvement Score Of Association Rules to
Know Size of Possibility Level Of Two Item Can
Buy Concurrently
To Get the Hypothesis Of
Marketing Strategy Of Cross-
Selling
RELATED WORK
Bugher (2000)• Making Data Tables To Determine
Frequency Of Each Product Item which is Sold With Another Product
Berry (2000)
• Determine Cross-Selling Products That Are Suitable For One Of The
Largest Banks In America which Has Millions Of Subscribers
Cashin (2003)• To View Clusters Of Clients Who
Have The Same Product Affinity
Adhitama (2010)
• Using The Technique Of Associative Rules Mining For Produce The Sales Strategy In An Indonesia's Largest
Retail Company
METHODS
Begin
Analyze Pre-processing Transaction Data
Analyze Frequent Item Set
Calculate Support
Calculate Confidence
Calculate Improvement
Determine Sales Strategy
END
Associative Rules
Mining
Framework Methods
METHODS
Threshold of item set created with trial and
errors methods (Adhitama, 2010)
Clustering K-Means
Calculate Support ScoreN: Number of Transaction
AnalyzePre-processing Data
Analyze Frequent Item Set
( )( )
X Ys X Y
N
Calculate Confidence Score
( )( )
( )
X Yc X Y
X
Calculate Improvement Score
( )( )
( ). ( )
s X Yi X Y
s Y s X
Determine Sales Strategy With Associative Rules
Scores And RFM Analysis
Start
RFM ANALYSIS• Recency: Based on The Recent
Date (Updated) Customer transaction
• Frequency: Based on The Quantity of Products purchased by the
customer
• Monetary: Based on The Value of Transaction made by the customer
Assumption: (The Monetary Process is done by multiplying the Quantity
of purchase with the variable of each product)
Begin
Analyze Recency from the newest transaction (binning
5)- the oldest transaction (binning 1)
Analyze Frequency from the highest purchase
transaction (binning 5) – the lowest purchase transaction
(binning 1)
Analyze Monetary by multiplying the quantity of
purchase with the variable of each product
Analyze Transaction Data (Recent Date, Quantity,
Value Of Sales)
End
Transaction Data
Analyze RFM (Recency, Frequency, Monetary)
Divide Customer Into 5 Binning for analyze Recency,
Frequency, and Monetary
METHODS
Flowchart of Clustering K-Means Methods
Begin
Determine Cluster Nominal
Calculate Centroid
Calculate Distance
Clustering Based on Minimum Distance
There is Moving Object
End
Yes
No
System Development
Begin
System Analysis (Bottom Up), Output:- System Description- Information needs analysis- System functional requirements
UML, Output:- Use Case Diagram- Activity Diagram- State chart Diagram- Class Diagram
System Implementation, Output:- Borland Delphi 7 - Power Designer 15.3 - MySQL - Ms Excel
Verification and Validation
End
NO
METHODS
DISCUSSION (PRE-PROCESSING DATA)
Product Clusters (Results of Pre-processing Data):
1. Passenger Bias (A)2. Passenger Broad Market (B)3. Passenger Broad Market Premium ( C )4. Passenger High Performance (D) 5. Passenger Ultra High Performance (E)6. Ultra Light Truck Radial (F) 7. Light Truck Radial (G)8. Ultra Light Truck Bias (H)9. Light Truck Bias (I)10. Bias Truck (J)11. EM A 21 Bias (K)12. EM A 3 A GDR Bias (L) 13. EM A 3 A LDR Bias (M)14. Front Farm Bias (N)15. Rear Farm Bias (O)16. Ground Tire Import (P)
(Results of Pre-processing Data): Clusters Based On Characteristics of Raw Material Products
*Data from a Tire Industry In Indonesia
DISCUSSION (DETERMINE SALES STRATEGY USING
ASSOCIATIVE RULES MINING)
1. Opportunity to sell product bundling in connection with
support value, especially combination products with a
score of small support (Adhitama 2010)
2. The biggest results of multiplication between
support and confidence can be used in determine strategy of
sales (Novrina 2010).
3. Segmentation customers are grouped according to
propensity scores, such as churn scores, cross-selling
scores, and so on, which are estimated by respective
classification (propensity) models (Konstantinos Tsiptsis
2009)
4. While for the combination of products E and F have
improvement score ≥ 1, indicating that the product E
and F are positively correlated, which means that if a customer buys E product, customers also agreed to buy the F product, otherwise if the
value of improvement score <1. (Adhitama 2010)
Support * confidence
0.1383
0.1418
0.1372
0.1817
0.1441
0.1303
0.1300
0.1371
0.1329
0.1382
0.1315
0.1301
0.1289
0.1285
RESULTS (DETERMINE SALES STRATEGY USING ASSOCIATIVE
RULES MINING)Result of Calculating
of Support ScoreProduct Support
Product A 0.01Product B 0.55Product C 0.42Product D 0.51Product E 0.11Product F 0.48Product G 0.48Product H 0.48Product I 0.51Product J 0.50Product K 0.04Product L 0.12Product M 0.07Product N 0.01Product O 0.16Product P 0.01
K-item set=2
If Antecedent then Consequent Support Confidence
Improve-ment
If Buy B Then Buy J 0.276 0.502 1.005
If Buy B Then Buy H 0.279 0.508 1.049
If Buy B Then Buy G 0.275 0.499 1.043
If Buy B Then Buy D 0.316 0.575 1.128
If Buy B Then Buy F 0.282 0.512 1.059
If Buy B Then Buy I 0.268 0.487 0.954
If Buy D Then Buy F 0.257 0.505 1.044
If Buy D Then Buy G 0.264 0.519 1.083
If Buy D Then Buy I 0.26 0.511 1
If Buy F Then Buy H 0.258 0.535 1.104
If Buy G Then Buy I 0.251 0.524 1.026
If Buy H Then Buy I 0.251 0.518 1.015
If Buy H Then Buy J 0.25 0.516 1.034
If Buy I Then Buy J 0.256 0.502 1.005
STEP 1 STEP 3
STEP 2 & 4
RFM ANALYSIS
All Of Strategy: For Customer With The Highest Scores of
Frequency and Recency (Binning 5)
Second Strategy: For customer With The Highest Scores Of
Monetary In B and D Products
Third Strategy: For Customer With The Highest Scores Of
Monetary In B, J, H, D, F, I Products
Four Strategy: For customer With The Highest Scores Of
Monetary In B and D Products
CONCLUSION
The Biggest Size Of Dominate Level Of Rule
Itemset Is When Customer Buy Product B (Passenger Broad Market
Product) With 55 % Support Score
In Generally, The Rules Of Itemset Have Confidence
Level> 50%
The Biggest Improvement score (1.128) Is If Buy B
(Passenger Broad Market) Then Buy D (Passenger
High Performance)
There Are 22 Rules Significantly For Used To
Determine Sales Strategy Of Cross Selling With Calculate Support, Confidence, And
Improvement Scores For Those Rules.
Sales Strategy of cross selling in this work created from support, confidence, and improvement scores.
Thank You
Data Mining
• Data Mining adalah serangkaian proses untuk menggali nilai tambah dari suatu kumpulan data berupa pengetahuan yang selama ini tidak diketahui secara manual.
• Data mining adalah proses untuk penggalian pola-pola dari data.
• Data mining menjadi alat yang semakin penting untuk mengubah data tersebut menjadi informasi (Margaretta, 2010)
Data, Informasi, Knowledge, Sistem
• Data merupakan suatu objek, kejadian, atau fakta yang terdokumentasikan dengan memiliki kodifikasi terstruktur untuk suatu atau beberapa entitas.
• Informasi merupakan suatu hasil dari pemrosesan data menjadi sesuatu yang bermakna bagi yang menerimanya, (Vercellis,2009)
• Pengetahuan adalah data dan informasi yang digabung dengan kemampuan, intuisi, pengalaman, gagasan, motivasi dari sumber yang kompeten (Hendrik, 2003)
• Sistem adalah suatu jaringan kerja dari beberapa prosedur yang saling berhubungan, berkumpul bersama-sama untuk melakukan suatu kegiatan atau menyelesaikan suatu tujuan tertentu. (Wawan dan Munir, 2006)
Proses Produksi Ban
Bagian-Bagian Ban
Tread •Bagian telapak ban berfungsi untuk mlindungi ban dari benturan, tusukan obyek dari luar yang dapat merusak ban
Breaker •Bagian lapisan benang (pada ban biasa terbuat dari tekstil, sedangkan pada ban radial terbuat dari kawat yang diletakkan diantara tread dan casing
Casing •Lapisan pembentuk ban, merupakan rangka dari ban yang menampung udara bertekanan tinggi agar dapat menyangga ban
Bead •Bundelan kawat yang disatukan oleh karet yang keras, melekat pada Pelek
Jenis-Jenis Ban
Ban Bias
• Dibuat dari banyak lembar dengan sudut carcass cord 40 sampai 65 derajat terhadap keliling lingkaran ban
Ban Radial
• Carcass cord membentuk sudut 90 derajat terhadap keliling lingkaran ban.
Ban Tubeless
• Terdapat lapisan dari karet lembek sintesis yang disebut innerliner. Lapisan ini akan mengurung udara dan membuat ban menjadi tubeless
Bahan Utama Pembuatan Ban
Karet•Alam•Sintesis
Kimia•Carbon Black•Crude Oil•Resins•Antioxidants•Sulfur•Accelerators
Carcass Materials
•Nylon•Rayon•Polyglass•Polyester•Flexten•Steel
Bead wires
•Steel/Iron
Karet
Karet Alam•Merupakan politerpena yang disintesis secara alami melalui polimerisasi enzimatik isopentilpirofosfat•Gugus kimia:
Karet Sintesis
•Karet Khusus yang dibuat dengan tujuan tertentu (meminimalisir kekurangan-kekurangan yang ada pada karet alam, Contoh: IIR (isobutene isoprene rubber)
METHODS
Trial and Errors Methods to determine thresholds of frequent itemset methods: The number of item sets (rules)
should be residing until half of amount of product classification (Adhitama, 2010).
With trials and errors methods, Parameter limits (threshold) determined that min_support = 24.75% and
min_confidence = 25% (result 8 the number of item sets in K=1 from 16 classification of product)
Cross Selling
Teknik menjual sesuatu barang/jasa yang berhubungan dengan suatu barang/jasa
Contoh: Seorang pembeli handycam, ditawarkan untuk membeli battery handycam, tas handycam, tiang untuk menyanggah handycam, dsb.
PELANGGAN A1 A2 A3 A4P1 10 26 31 446P2 7 25 16 266P3 9 25 21 249P4 9 27 42 213P5 3 24 37 419P6 3 29 43 238P7 5 24 37 438P8 8 32 21 348P9 10 24 12 279P10 8 28 28 342
Akan diklaster menjadi 2 klaster1. Klaster A2. Klaster B
Metode K-Means::
ALGORITMA K-MEANS CLUSTERMula
i
Menentukan Jumlah Klaster
Menghitung Centroid
Menghitung Distance
Kelompokkan Berdasarkan Jarak
Minimum
Ada Objek Berpindah
Selesai
Ya
Tidak
MENGHITUNG CENTROID
• Untuk centroid pertama, dua data pertama dianggap sebagai centroid bisa juga diacak mana yang pertama
• Centroid:– P1 (10, 26, 31, 446) c1 (10, 26, 31, 446)– P2 (7, 25, 16, 266) c2 (7, 25, 16, 266)
MENENTUKAN JUMLAH KLASTER K = Jumlah Klaster K = 2
MENGHITUNG JARAK
• Menggunakan rumus Euclidean Distance
• P1 terhadap c1
• P2 terhadap c1
• Dan seterusnya…
MENGHITUNG JARAK…
• P1 terhadap c2
• P2 terhadap c2
• Dan seterusnya…
MENGHITUNG JARAK…
• Hasilnya disusun dalam Matriks (D0)
c1
c2
KELOMPOKKAN BERDASARKAN JARAK MINIMUM
• Akan Menghasilkan Matriks G0
Klaster 1
Klaster 2
• Untuk iterasi 1• Klaster1 = P1, P5, P7• Klaster2 = P2, P3, P4, P6, P8, P9, P10
MENGHITUNG CENTROID BERIKUT
• Untuk centroid berikutnya dihitung berdasarkan masing-masing kelompok
• Centroid kelompok 1:
• Centroid kelompok 2:
MENGHITUNG JARAK
• Hasilnya disusun dalam Matriks D1
c1
c2
KELOMPOKKAN BERDASARKAN JARAK MINIMUM
• Akan Menghasilkan Matriks G1
• Karena G0 = G1, Maka iterasi diberhentikan, karena tidak ada objek yang berpindah
• Jadi, Klaster Akhir adalah• Klaster1 = P1, P5, P7• Klaster2 = P2, P3, P4, P6, P8, P9, P10
Klaster 1
Klaster 2
Frequent Item Set Calculation
K Item Set=1 (1 unsur)
Perhitungan Frekuen Item Set
F1 = {{A}, {B}, {C}, {D}, {E}, {F}, {G}}
K Item Set=2 (2 unsur)
Lanjutan Frekuen Item Set
Hasil Perhitungan
Untuk K=3 (3 Unsur) himpunan yang mungkin terbentuk
Perhitungan K=3
Dari tabel-tabel di atas, didapat F3 = { }, karena tidak ada Σ >= Ф sehingga F4, F5, F6 dan F7 juga merupakan himpunan kosong.
Support Calculation
Support A (Passenger Bias)= 2/5 = 0.4=40 %
Support AB= 2/5 = 0.4=40%
transaction IDPassenger Bias (A)
Passenger Broad Market (B)
Passenger Broad Market Premium (C)
Passenger High Performance (D)
1 1 1 0 0
2 0 1 1 0
3 0 0 0 1
4 1 1 1 0
5 0 1 0 0
Confidence Calculation
Confidence AC= 1/2 = 0.5=50%
transaction IDPassenger Bias (A)
Passenger Broad Market (B)
Passenger Broad Market Premium (C)
Passenger High Performance (D)
1 1 1 0 0
2 0 1 1 0
3 0 0 0 1
4 1 1 1 0
5 0 1 0 0
Improvement Calculation
Support A= 2/5= 0.4Support C= 2/5= 0.4Support AC= 1/5= 0.2
Improvement AC= 0.2/(0.4*0.4) = 1.25 (Positive Correlated)
transaction IDPassenger Bias (A)
Passenger Broad Market (B)
Passenger Broad Market Premium (C)
Passenger High Performance (D)
1 1 1 0 0
2 0 1 1 0
3 0 0 0 1
4 1 1 1 0
5 0 1 0 0
No. Distributor Binning Recency Binning Frekuensi Binning Monetary
1 ARVIAPRATAMA TIARA PT Total 5 1 85 I + 100 G
2 SURYA JAYA CV Total 1 5 2041 I + 1731 J + 80 O + 12 K + 158 L
3 OTO SENTOSA SENTRA MAKMUR PT. Total 2 4 1733 I + 581 J + 129 G + 64 C + 1 K
4 UTAMA SERVICE STATION Total 1 1 15 G + 31 C + 10 D + 5 E + 78 B + 20 F
5 SINAR REJEKI JAYA CV. Total 3 5 1930 I +2448 J + 600 H + 10 D +1246 B
6 SULUNGBUDI ABADI PT Total 3 1 52 H + 104 B + 40 F
7 P R I M A CV Total 3 2 292 I + 15 J +78 H+ 104 B +10 F
8 BANOLI MOTOR PT. Total 3 3 50 I + 143 J + 556 H + 66 G +226 B + 80 F
9 M A J U UD Total 3 4 778 I + 232 J + 15 G + 193 C + 1019 B
10 CANDRA BUANA MANDIRI PT. Total 1 5 1587 I+ 2015 J + 906 H + 575 G + 214 C + 10 D +955 B +500 F
11 SAMUDRA UD Total 3 4 1824 I + 514 J + 208 H +851 B +6 M
12 ANDI MOTOR UD Total 3 4 1582 I + 865 J + 104 H + 25 G +13 B +50 F
13 CAHAYA SURYA CV Total 3 4 1373 I + 572 J + 57 G +1001 B + 200 F +24 L
14 ANEKA RAYA CV. Total 4 3 748 I + 176 J +200 B
15 SALAWATI MOTORINDO PT Total 5 1 315 I + 20 J + 65 B +2 K
16 PERKASA BAN TOKO Total 4 3 1000 I + 36 J + 52 H + 77 G + 32 D +20 O
17 LINDA HANTA WIJAYA PT (Bppn) Total 2 5 1638 I + 1720 J + 104 H +148 G + 189 C + 25 D + 71 B + 400 F + 12 O + 24 K + 114 L+ 34 M
18 RODA MAS PD. Total 3 3 599 I + 444 J + 78 H + 181 G + 26 C + 16 D + 20 E+ 301 B
19 KARYA SUKA ABADI PT(Jambi) Total 4 3 730 I + 76 J + 75 G + 33 C+ 91 B+ 25 F+ 44 O
20 CENTRADIST PARTSINDO UTAMA PT Total 3 4 938 I + 367 J + 500 H + 100 G+ 1504 B + 150 F+ 48 O + 2 M
21 KARYA SUKA ABADI PT(Padang) Total 5 3 775 I + 101 J + 25 G + 80 C + 10 D + 20 E + 590 B + 33 F
22 KOPERASI KARYAWAN GOODYEAR Total 1 1 6 H + 14 G + 13 C + 11 D + 4 E + 39 B + 2 F
23 SAPUTAN ADIJAYA MOTOR PT Total 1 1 110 I + 30 H + 91 B + 8 L
24 EKA SARI LORENA TRANSPORT PT Total 2 1 25 J
25 LAJU JAYA CV Total 2 5 2068 I + 1014 H + 279 G +41 C + 237 D + 8 E + 2842 B + 600 F
26 SINAR JAYA GEMILANG PT Total 4 2 254 I + 86 J + 20 G + 20 D + 130 B + 20 F
27 LAKSANA CIPTA RAHARJAPT Total 4 1 100 I
28 ANEKA PRIMA INTERNUSAPT Total 4 2 500 I + 26 H + 45 G + 90 B + 20 F +122 O
29 KARYA SUKA ABADI PT(Palembang) Total 2 4 1502 I + 489 J + 52 H + 76 G + 50 C +16 D +286 B +150 F + 66 O + 24 L
30 LINDA HANTA WIJAYA PT (Smd) Total 1 3 677 I + 478 J + 78 H +67 G + 41 D + 78 B + 125 F + 12 K + 60 L
31 KARYA SUKA ABADI PT(Pekanbaru) Total 4 3 629 I + 45 J + 182 H + 55 G + 13 C + 57 D + 95 B + 125 F
32 PUTRA ANDALAS NUSANTARA PT. Total 5 2 341 I + 140 J + 61 G + 26 C + 50 D + 260 B +25 F
33 ANEKA BAN PERMAISURI TOKO Total 2 1 5 H+ 10 G + 13 C + 26 B + 10 F
34 CHRISTA KARYA MANDIRI PT Total 4 4 642 I + 124 J + 52 H + 85 G + 162 C + 41 D +1379 B +250 F
35 CANDRA BUANA MANDIRI PT (JKT) Total 1 2 323 I + 2 E + 256 B
36 SARI LORENA PT Total 2 1 85 J
37 KIAN HWA WELLY SETIAWAN Total 4 1 63 B
Result Of RFM Analysis
Direct Marketing Association (DMA) in 1991 to determine
one-to-one between product categories.
Proposed three price scenarios that can be applied, namely: "Together"
(for example, "buy X and Y with separate $__")," prices" (e.g., "buy X for $ __, and get only the price of Y $__"), and "freebie" (for example,
"buy X for $ __, and Y-free") (Harlam 1995)
Related Work
References• Adegboyega Ojo and E. Estevez (2005). Object-Oriented Analysis and Design with UML, e-
Macao.• Adhitama, B. (2010). "Determining the sales strategy using the association rules in the context
of crm."• Berry, M. J. A. a. L., G. S. (2000). Mastering Data Mining – The Art and Science of Customer
Relationship Management. New York, Jhon Wiley and Sons.• Borland, C. (2002). Borland Delphi. version 7.0. B. 4.453.• Bugher, G. (2000). "Market Basket Analysis of Sales Data for a client of Cambridge Technology
Partner." Megaputer Intelligence Inc., available • Cashin, J. R. (2003). Implementation of A Cross-Selling Strategy for A Large Midwestern
Healthcare Equipment Company. Department of Psychology, Southern Illinois University at Carbandole.
• FOLDOC (2001) Unified Modeling Language. • Harlam, B. A. e. a. (1995). "Impact of Bundle Type, Price Framing and Familiarity on Purchase
Intention for the Bundle." Journal of Business Research, 1995, 33, pp. 57-66.• Jianxin(Roger) Jiao, Y. Z., & Martin Helander (2006). "Analytical Customer Requirement Analysis
Based on Data Mining." Idea Group Inc.• Konstantinos Tsiptsis, A. C. (2009). Data Mining Techniques in CRM: Inside Customer
Segmentation. West Sussex, Wiley.• Microsoft, I. (2007). Microsoft Excel 2007.• Novrina (2010) Association Rule (Algoritma a Priori). • O’Brien (2008). Introductory Business Information Systems Perspective Edition 7. New York, Mc
Graw Hill.• Oracle (2011). MySQL.• Sybase, I. (2010). PowerDesigner Studio Enterprise Standalone local. 15.3.0.3248.• Witten, I. H. a. F., E. (2005). Data Mining – Practical Machine Learning Tools and Techniques 2nd
Edition, Morgan Kaufmann Publisher.
UNIFIED MODELLING LANGUAGE
Oleh:TRIYODA ARRAHMAN
UNIFIED MODELLING LANGUAGE
Visualisasi
Merancang
Mendokumentasikan sistem piranti lunak
UML menawarkan sebuah standar untuk merancang model sebuah sistem
UNIFIED MODELLING LANGUAGE
UML mendefinisikan diagram-diagram berikut ini :
• use case diagram • class diagram • behaviour diagram :
-- statechart diagram-- activity diagram
• interaction diagram :-- sequence diagram-- collaboration diagram
• component diagram • deployment diagram
Use Case Diagram
• The Function from Use Case shows a set of actors and use cases, and their relationships (Adegboyega Ojo and Estevez 2005)
Activity Diagram
• Activity diagram is used to describe the workflow activities in the system, in other words is how systems perform certain functions
Statechart Diagram
• In general statechart diagram describes some certain class (one class can have more than one diagram statechart).
Class Diagram
• Class diagram is the main diagram in object-oriented modeling. Class diagrams are used to show static structure of the system. The class is a collection of objects that have attributes and behavior (operations) which similar
UML (UNIFIED MODELING LANGUAGE)
The Unified Modeling Language (UML) is used to specify, visualize, modify, construct and document the artifacts of an object-oriented software-intensive
system under development (FOLDOC 2001).
Use case diagram
Menggambarkan fungsionalitas yang diharapkan dari sebuah sistem
Yang ditekankan adalah “apa” yang diperbuat sistem, dan bukan “bagaimana”
Sebuah use case merepresentasikan sebuah interaksi antara aktor dengan sistem.
Use Case Diagram
Marketing Supervisor
Input Transaction Data
Analyze Frequent ItemSet
Calculate Support
Calculate Confidence
Calculate Improvement
Log In PSP1 Program
Actor
Case
Association
Dependency
Activity Diagram
Menggambarkan berbagai alir aktivitas dalam sistem yang sedang dirancang, bagaimana masing-masing alir berawal, decision yang mungkin terjadi, dan bagaimana mereka berakhir.
Activity Diagram
[Salah]
[Benar]
Customer Marketingt Officer Supervisor pemasaran Admin Manager Pemasaran
[Salah][Salah]
[Benar][Benar]
Melakukan Transaksi Mendata data transaksi
Melaporkan data transaksi
Melakukan Log In Program PSP1
Menginput Data Transaksi
Menganalisis Frequent itemset
Menghitung Support
Menghitung Confidence
Menghitung Improvement
Menetapkan Strategi Penjualan Cross Sell ing
melakukan penjualan bundle, paket promosi produk
Menerima laporan data transaksi
Mengenali perilaku transaksi pelanggan secara mendalam Mencapai target penjualan cross sell ing
Authentification
Benar_Salah
Melakukan evaluasi target penjualan cross sell ing
Begin
End
Flow Activity
Swim lane
StateChart Diagram Menggambarkan transisi dan perubahan keadaan (dari satu state ke state lainnya) suatu objek pada sistem sebagai akibat dari stimuli yang diterima
Menggambarkan class tertentu (satu class dapat memiliki lebih dari satu statechart diagram).
Statechart Diagram
[Password dan username benar]
[memulai program]
[Input Password kembali]
[Confirm terproses]
[Input NIP Success]
[Submit data]
[Password and username salah]
[Cancel or Quit]
Input Username and password
entry / Username and password...
authentification
do / authentification...
Memasuki Program
do / Masuk Home...
Confirm To Admin
do / confirm...
Get Password
do / dapatkan password...
input NIP Supervisor
entry / NIP...
Condition
State
Transition
Class Diagram Sebuah spesifikasi ,
inti dari pengembangan dan desain berorientasi
objek
Gambaran keadaan (atribut/properti) suatu
sistem, sekaligus menawarkan layanan untuk memanipulasi
keadaan tersebut (metoda/fungsi)
Struktur dan deskripsi class, package dan
objek beserta hubungan satu sama lain seperti
containment, pewarisan, asosiasi, dan lain-lain.
Class Diagram
Class memiliki tiga area pokok :• 1. Nama (dan stereotype)
2. Atribut3. Metoda
Atribut dan metoda dapat memiliki salah satu sifat berikut :
• Private, tidak dapat dipanggil dari luar class yang bersangkutan
• Protected, hanya dapat dipanggil oleh class yang bersangkutan dan anak-anak yang mewarisinya
• Public, dapat dipanggil oleh siapa saja
Class Diagram0..1
Rules0..*
Rules Support
0..1rules
0..1rules confidence
0..1Rules
0..1Rules Improvement
0..*data produk
0..*data transaksi
0..*data customer
0..*data transaksi
0..1nilai improvement
0..1Nilai improvement
0..1Nilai Confidence
0..1Nilai confidence
0..1nilai support
0..1Nilai support
0..1Rules
0..1Rules
1..*strategi penjualan
0..*strategi penjualan
0..1jumlah nominal penjualan
0..*jumlah nominal penjualan
0..1strategi penjualan
0..*strategi penjualan
0..1Data transaksi
0..*Data transaksi
0..1Data transaksi
0..*Data transaksi
0..1target penjualan
0..*target penjualan
Customer
++
Nama CustomerArea
: std::string: std::string
+ Melakukan transaksi ()...
: void
Marketing Officer
+-
NamaData Transaksi
: std::string: std::string
--
Mendata data transaksi ()Melaporkan data transaksi ()...
: void: void
File Transaksi
++++--
Nama CustomerAreaJenis Produk orderGolongan produk orderNomor TransaksiTanggal transaksi
: std::string: std::string: std::string: std::string: int: int
- Menyimpan data transaksi ()...
: void
Supervisor Pemasaran
-----
UsernamePasswordData transaksistrategi penjualantarget penjualan
: std::string: std::string: std::string: void*: int
-
-
+-
melakukan Log In program PSP 1 As Pengguna ()
mencapai target penjualan Cross Sell ing ()
mengolah strategi penjualan ()menginput data transaksi ke dalam program PSP1 ()
...
: void
: void
: void: int
Program Penentuan Strategi Penjualan
----
Rules Item SetNilai SupportNilai ConfidenceNilai Improvement
: int: int: int: int
- Menentukan Strategi Penjualan dengan mengolah rules, support, confidence, improvement ()
...
: void
Admin
--
UsernamePassword
: std::string: std::string
--
Log In As Admin ()Revisi data ()
: void: void
Perhitungan Frequent Item Set
---
Himpunan Item setBilangan item setdata transaksi
: int: int: int
- Menentukan rules item set ()...
: void
Perhitungan Support
---
Rules Item SetJumlah transaksi item setjumlah transaksi
: int: int: int
- menghitung support ()...
: int
Perhitungan Confidence
--
-
Rules Item SetNilai support Base produk union additional produk
Nilai support base Produk
: int: int
: int
- menghitung nilai confidence ()...
: int
Perhitungan Improvement
--
--
Rules Item setNilai support Base produk union additional produk
nilai support base produknilai support additional produk
: int: int
: int: int
- menghitung nilai improvement ()...
: int
Manager Pemasaran
--
Target Penjualan Cross Sell ingJumlah nominal penjualan
: int: int
-
+
Evaluasi target penjualan Cross Sell ing ()
mengawasi jumlah nominal penjualan ()...
: int
: int
Produk
++
Jenis ProdukGolongan Produk
: std::string: std::string
Penjualan Cross sell ing
--
Strategi penjualanJumlah nominal penjualan
: void*: int
+ menerapkan strategi penjualan cross selling dalam penjualan ()
...
: voidClass
Association
Database (Hasil Generate Class Diagram)
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