Customer Relationship Management for Determining of Sales Strategy Using Association Rules Mining...

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)