ARMBA Associative Relations in Indotoko

CHAPTER I

INTRODUCTION

1.1 Description of the Problem

Nowadays Indonesia as “industry country” that is so many company there.

And in Indonesia, there are so many companies that offer retail sales of products

owned by personal retail and other companies. One example of the personal is

Indotoko. Here why Indotokocalled as personal, because the retail is standalone,

not in every area is available, and not endorse with the other companies. Some

examples of retail that endorse with other companies is Indomaret, Alfamart,

7eleven, Circle K, etc. because the retail almost available in every area that easily

to find. Products are sold to virtually complete, ranging from baby supplies,

personal equipment, household goods, and many more who may not mention here.

Initially the company was just grew and grew up in Java. Because the

magnitude of the needs of the community in consuming goods and services, then

Indotokocompany started expanding. First time established, Indotokois just small

retail. Then after the owner succeeds with the business, he was enlarging the retail

become bigger and larger than before after some years established. Indotoko has

been expanding into two another branches in Yogyakarta recently, but today

comeback again to be one and the only one retail, but it expanded to be larger.

The results of this expansion were to get a positive response from the community

who can be seen from the height of the existing Indotoko visitors in each area.

Then also, the loyalty is one of much important things that must we get from the

customers to make sure that the customers do not choose the other options of the

retail, we can call it fixed customers. In this era company has been growing up

even just Retail Company.

In this research we analyze about member card. While providing

discounts to the customer, these cards allow the retailer to develop a better

understanding of individuals' purchasing habits by associating customers with

transactions. The uses of this information vary, but may include informing

product placement decisions, designing personalized marketing campaigns, and

determining the timing and extent of product promotions among others. (Raeder,

Chawla)

1.2 Problem Formulation

1. How is the associative relations that happened between items in Indotoko?

2. How is the problems solutions of the retail card member in Indotokobased on

analysis of AR-MBA?

1.3 Research Objectives

The objectives of this research are:

1.To know the associative relations that happened between items in

Indotoko.

2. To know the solutions of the retail card member in Indotokobased on

analysis of AR-MBA.

CHAPTER II

LITERATURE REVIEW

2.1 Deductive Study

2.1.1 AR-MBA

Association in data mining is the work to determine which attributes will be

obtained simultaneously. In the business world the term is commonly known as

affinity analysis. The task of the asociation rule is to find a rule that does not

cover to measure the relationship between two or more attributes.

Association rule is a form if the "previous incident" and "consequences".

(IF antecedent, THEN consequent). Along with the calculation of support and

confidence of rules. The pattern of association to be one of the most interesting

functionality in extracting data (Kumar and Wahidabanu, 2007).

Association Rule is a data mining technique to find the associative rules

between combinations of items. Examples of Association Rule of purchases in a

purchase analysis is able to know how likely a customer buys the same coffee

with sugar. With this knowledge owner can adjust the placement of the goods or

designing a marketing campaign using a combination of discount coupons for

certain items.

One example of application of Association Rule is Market Basket

Analysis. Association Rule became known for its application to analyze the

contents of the purchase shopping cart, so the Association Rule is also commonly

referred to as Market Basket Analysis. Association Rule also known as one of data

mining techniques that became the basis of a variety of other data mining

techniques.

Each consumer buys a set of different items, in different amounts, and in a

different time. Market Basket Analysis using information of something that

purchased by consumers to provide a signal or information that is who they are

and why they made the purchase? Market Basket Analysis provides an

understanding of the merchandise by telling us which products are possible to be

purchased simultaneously and which product is approved to be promoted. This

information can be used in:

1. More profitable advertising and promotion. Market Basket Analysis using advertising

and promotion in order to understand better how shoppers respond to and

communicate over the products offered, for the purpose of the retailer "How do I

change this sale? What else is sold and what was advertised ".

2. More precise targeting in return ROI (Return on Investment). Market Basket Analysis

is used to optimize campaigns and promotions to increase sales and margins by

targeting more precise.

3. Loyalty card promotions with longitudinal analysis. Longitudinal Market Basket

Analysis enables users to buy the characters retailer customer behavior over time.

Retailers use loyalty cards to capture the lifecycle of data so that they can analyze the

purchasing behavior of customers such as shopping. For example a toy retailer

explained that he did not make sense to sell a game engine (with a slight margin)

except for customers who also buy the game software and accessories (high margin).

They use the Market Basket Analysis of loyalty card data to determine their overall

margin on sales of video games and promotions to make the memory of the customer

and affect a buyer to purchase games and accessories from them and not from other

retailers.

4. Determine the layout of the new store (new store layouts) or attract more traffic to the

store, set of products which will be placed in a special place. Market Basket Analysis

also uses the space to improve traffic planning and visual merchandising to boost sales.

5. Identify when the problem in pairs / coupon (issue coupons). To increase sales or

spending items into inventory.

There are a lot of definitions of Market Basket Analysis that use to known. Such

as Market Basket Analysis a mathematical technique used by marketing

professionals to express the similarity between the individual products or product

groups. Market Basket Analysis with respect to a set of problems related to flying

businesses to find out from the point of sale transaction data. Market Basket

Analysis is a general term for the methodology of the study of the composition of

the basket of groceries purchased by households during a time of shopping.

Market Basket Analysis is a collection of a combination of products purchased

together. Market Basket Analysis trend analysis of an item bought by the same

customer at the same time. Market Basket Data is data that describes the

transaction underlying the three different entities, namely Customers, Orders /

purchases, and Items (goods)

Introduction of the customer at any time make it possible to instantly

recognizable, such as the frequency of purchases made by the customer. Three

levels of market basket data is important to understand the request quickly. These

measurements give an idea for a business. In some cases, there are some repeat

buyers, so that the proportion of the purchase of every customer close to 1. This

suggestion is used a company to increase sales per customers. Or the amount of

each purchase of products close to 1, suggestion can be an opportunity for cross-

selling during the purchase process. Important whether or not an associative rule

can be determined by two parameters, support (the support) is the percentage of

the combination item page and confidence in the database (the certainty) that the

strength of the relationship between items in the associative rule.

So, our research held in Indotokoat Jalan Kaliurang km. 5, Sleman,

Yogyakarta. There are 100 pieces of receipt of goods purchases that already

collected. Conducting Pre Data Processing, Data Processing, Establishment of

Association Rule. For application master, we use Microsoft Excel, Microsoft

Visio, and Rapid Miner software.

2.1.2 Card Member

According to (Miguel, Camanho, Joao Falcao) The establishment of loyalty

relationships with customers became a main strategic goal for this company. The

development of the company’s information system and the implementation of a

loyalty program have enabled collecting data on each customer profile (e.g.

customer name, address, date of birth, gender, number of people in the household,

the telephone number and the number of one identification document) and on their

transactions (date, time, store, products and prices). This programm is supported

essentially by a loyalty card, and currently approximately 80% of the total number

of transactions is done by customers using the loyalty card. At present, the

company customers are segmented in two ways. One of them consists on grouping

customers based on their shopping habits. This segmentation model is a simplified

version of the RFM model proposed by Bult and Wansbeek [20], and is called

internally: “frequency and monetary value” (FM) model. According to the values

of these two variables, the company specifies 8 groups of customers. Each client

integrates one of these groups, according to the average number of purchases done

in a 8 week period and the average amount of money spent per purchase. The

changes in the percentage of customers belonging to each group are used to sinal

actions required in customer relationship management. For example, if the number

of customers in the clusters with more visits to the store decreases, the company is

alerted to launch marketing campaigns in order to motivate customers to go to the

stores more often. The other method of segmentation is based on customer

necessities and preferences. In this case, customers are grouped into 7 segments

according to the mix of categories and products they purchase. Each segment is

distinguished by the high relative weight of the purchases of certain products

category compared with others segments. This type of segmentation is used to

optimize the price and range of products sold in certain stores. Mining Customer

Loyalty Card Programs 9 Concerning the promotional strategy of the company,

there are mainly 3 types of policies:

1. discounts on specific products advertised in the store shelves and leaflets,

that are applicable to all customer with a loyalty card;

2. discounts on purchases done on selected days (percentual discount or

absolute

discount on the total value of purchases). These are applicable to

customers that present at the point of sale (PoS) the discount coupon sent

by mail;

3. discounts for specific products on selected days. These can be sent by mail

or issued at PoS.

The first two types of promotions do not differentiate between customers of

different segments. The third type, instead of using the segmentation models

previously described, uses a model based on the historical purchases of the

product included in the promotion. The discounts are only issued to the most

frequent buyers of the product, or to those customers who do not normally buy

the product, to encourage new buyers.

The analysis reported in this paper is based on transactional data of customers with a

loyalty card. The database used includes the records from the last trimester of

2009. Each transaction includes: the client identity number, the date and time of

the transaction, the product transacted and the price of the product. In addition to

the transactions information, the company provided demographic information for

each customer: residence postcode, city, date of birth, gender, number of persons

in the household. The preparation of the database for the exploratory analysis

involved the integration of the data from different sources, and the elimination of

the outliers. Customers whose average amount of money spent per purchase or

the average number of purchases per month is out of the range of the mean plus

three standard deviations were excluded from the analysis. As we are interested in

the design of promotions for households it was also decided to remove from the

database all customers whose average amount of money spent per purchase was

greater than e500. These represented 0.75% of the customers included in the

original database. Usually, purchases exceeding this value are done by small

retailers that resell the products in competing stores, so these customers are not

intended to be included in promotional programmes. After the selection process,

the database contained 2.142.439 customers.

2.2 Inductive Study

The field of market basket analysis, the search for meaningful

associations in customer purchase data, is one of the oldest areas of data mining.

The typical solution involves the mining and analysis of association rules, which

take the form of statements such as people that buy diapers are likely to buy

beer." It is well-known, however, that typical transaction datasets can support

hundreds or thousands of obvious association rules for each interesting rule, and

filtering through the rules is a non-trivial task. (Raeder, Chawla)

Association rule mining (ARM) is used for identification of association

between a large set of data items. Due to large quantity of data stored in

databases, several industries are becoming concerned in mining association rules

from their databases (Gupta, Mantora 2014). This analysis may be carried out on

all the retail stores data of customer transactions. These results will guide them to

plan marketing or advertising approach. Market basket analysis will also help

managers to propose new way of arrangement in card member

CHAPTER III

RESEARCH METHOD

3.1 Object of the Research

The company is a kind of retail, named Indotokoand the owner is Mr. Bambang.

Indotokohas stood since year 2009 in Jl. Karangwaru Lor, Yogyakarta. We use card

member analysis and minimum collection 101 receipts to know the department.

Company Profile

Name : Indotoko

Owner : Bambang Sudjatmiko

Address and phone : Jl.Karangwaru Lor

Post Code : 55514

Established : Year 2009

3.2 Collecting Data Method

In this research, the collecting data method used is:

a. Observation

Researcher do the observation directly in the Indotokoto know how the layout of the retail.

b. Interview

Researcher do interview to the owner to know the profile of the company and to permit

for allowance of collecting receipt.

c. Receipt collecting

Researcher collecting the receipt of customer purchasing to know what department is often

bought by customer in Maju Swalayan. We collects 101 receipts for getting the fixed

and best result.

3.3 Types of Data

a. Primary

Collecting data method can get directly with interviewing and collecting 100 receipts of

customers purchasing in Indotoko. Method that used by researcher is AR-MBA

(Association Rule and Market Basket Analysis). From observation we set the Indotokoas

the observation place and directly research there. From interviewing the owner, we get

the profile of the retail and allowance to collecting receipts of customers purchasing.

b. Secondary

In secondary based on International Journal of Information and Computation

Technology (Gupta and Mantora 2014) Support = 20%, Confidence = 80%

Association rules are considered useful if they satisfy both a type equation here minimum

support threshold and a minimum confidence threshold that can be set by users or domain

consultants.

3.4 Flowchart

Herewith the flowchart process based on our research in the Maju Swalayan:

Figure 1 Flowchart Process

Data Collection

Implementation of Observation

Conclusion & Recommendation

Information:

Practicum Flow:

The description of practicum explains as follows:

1. Firstly, we do identification and learning deeply about the case that we want

identify.

2. Secondly, after we make problem statement, we do technical practice which

retailer that we want observe.

3. Thirdly, if the observation place already fixed, we can start to collect the data

transaction (bill) with minimum 100 bills.

4. Fourthly, as much as >100 bills that we got, we do data cleaning process. In this

process, we eliminate the bills which bill didn’t met the requirement like error

transaction, transaction that only show same department (1 department) and if the

transaction just for 1 item.

5. After we obtained 100 bills that already slip through requirement, we can execute

the data using xl-miner.

6. The processing data we do frequently based on the practicum module.

7. From the data that already processed, we can do analysis which department that

has strong relation.

8. After we know about which department that related each other, we can give

recommendation to the retailer.

9. We also can apply the model for make a member card as the study case that our

group got.

4.1 Initial condition of the research object

In our retail which is Indotoko, there is no member card at the store. According to

our discussion with the staff of the indotoko there are several problems why they didn’t

make the member card. One of the reason why they didn’t conduct the member card is

because indotoko is the one and only retail which is in region Yogyakarta and also the

placement of the retail isn’t strategic enough which they just can reach the customer

around of the region of the retail which is Karangwaru Lor. The another reason is because

there is no previous research about the customer which is related to the card member, so

they didn’t make the member card until now.

4.2 Rapid Miner Output

4.2.1 Data of Transactions

Table 4.1 Data of Transactions

no Type

1 pucuk harum, ultra uht, nyam2

2 sprite btl, marlboro, u mild

3 enaak, djarum spr filter, lucky strike

4 diapet, good day, GG filter

5 paramex, bendera, taro, so nice

6 sania, rose tepung, mi sedap, nutrisari, lifebuoy, dji sam soe

7 campina, dunhill

8 dara mi pipih, panda kc atom

9 ultra uht, dji sam soe

10 sampoerna, chunky bar, bendera

11 sari roti, soba mi, beng2, roma, malkis, top

12 sapu lantai, lap

13 pocari, larissa

14 white koffi, djawa bakery, GG signature

15 tebs, LA Filter

16 nestle btl, aqua, the kotak, pantene

no Type

17 malkis, calpico

18 white koffi, ladaku, masako

19 indomilk, bigbabol

20 so nice, marina comp powder, nano nano

21 malkis, sari roti

22 coffemix, class mild

23 hemart, gula jawa

24 kopiko, club, plastic

25 GG surya, bigbabol

26 kiwi shoe, sikat cuci, ichi ocha, okky koko drink

27 piattos, taro, champ, nu greentea

28 sarimi, mi sedap, bon cabe, uc1000

29 aguaria, padimas bolu

30 so nice, real good

31 marlboro, roma, trenz

32 kaki3, mi gemez

33 ultra uht, uc1000

34 mi sedap, telur ayam

35 indomi, so nice, bon cabe

36 cling, kara

37 real good, okky koko

38 champ, timtam, pendekar biru

39 mi sedap, indomi, the jawa

40 swallow agar, citra, pixy

41 top, timtam, kertas kado

42 tiniwinibiti, yupi

43 daia, davos

44 gillete, dji sam soe

45 campina, pocari

46 ultra uht, mi telur kuning, aqua

no Type

47 mi sedap, indomi, yakult

48 top kopi, djarum 76

49 top, xylitol

50 mirai ocha, okky koko, indomi

51 sonice, detoys

52 masako, ladaku, blue band

53 pocari, mamy poko, plastik, tjatoet, anlene

54 bango, kara, torabika

55 top, cafela

56 morita selai, bendera

57 nice fac non perfum, sandal swallow, dunhill

58 aguaria, sinsilk, intro filter

59 aguaria, sampoerna

60 bendera, mamy poko, ral good, djarum 76

61 risotto bubur, so nice

62 campina, aqua

63 biogesic, ultra uht

64 gatsby, lifebuoy, madurasa, s/m stmj, GG surya, hydro coco

65 h&s sampo, stella, dahlia kamp

66 believing, indomilk

67 kara, campina

68 attack, dettol

69 prambaru sandwich, campina

70 pulpy, twistko, oops kreker, yupi

71 nabati, top, roma, yupi

72 sobami, ramene, chacha

73 rio the, campina

74 mi sedap, sarimi, biskitop

75 prambaru sandwich, djawa bakery, s/q chocho cashew, a motion

76 medisoft cotton balls, daia

no Type

77 aqua, Marlboro

78 roma, enak, taro, djawa bakery, u mild

79 nissin fryschip, week n roti

80 indomilk, mentos

81 tiniwinibiti, kraft, pop mi, mi sedap

82 sariwangi, masako

83 telur ayam, bango, bendera, soklin, GG surya

84 campina, yakult

85 nissin lemonia, kremer kental, stella

86 pepsodent, biore, formula

87 dancow, indomi, royco, uc1000, tango wfr, nabati

88 citra, gastby, yakult

89 bon cabe, perfecta kurma, h&s sampo

90 inaco, oreo, indomilk

91 monde, gastby, ponds

92 paramex, bendera, taro, so nice

93 champ, bendera

94 marlboro, aqua

95 roma, nabati, kertas kado

96 rinso, ekonomi

97 believing tawar, LA Filter

98 kiss, aguaria, floridina, pucuk harum, shapes cheezy, paseo

99 good day, s/m susu jahe

100 modern ctn bud, snickers

4.2.2 Departement

Table 4.2 Departement

Department Information

Departemen 1 (Bumbu

Dapur Cair)sania, kara, bango, blue band, hemart

Departemen 2 (Bumbu

Dapur Kering)bon cabe, royco, masako, ladaku, swallow agar, gula jawa

Departemen 3 (Bahan

Makanan)so nice, champ, telurayam, shapescheezy

Departemen 4 (Tepung) padimas bolu, rose tepung

Departemen 5 (Makanan

Ringan)

enaak, taro, panda kc atom, sobami, piattos, mi gemez,

ramene, twistko, oops kreker

Departemen 6 (Roti dan

Pelengkapnya)

believing tawar, week n roti, prambarusandwich, djawa

bakery, madurasa, morita selai, sari roti, kremer kental,

perfecta kurma

Departemen 7 (Biskuit)nyam2, roma, malkis, trenz, tiniwinibiti, nissin frychip, nissin

lemonia, timtam, nabati, kraft, tango, monde, oreo, biskitop

Departemen 8 (Permen)kiss, yupi, mentos, pendekar biru, kopiko, nanonano,

bigbabol, davos, inaco

Departemen 9 (Coklat) chunkybar, beng2, top, chacha, s/q chocho chasew, snickers

Departemen 10 (Mie

Instant)

indomi, pop mi, misedap, dara mi pipih, mi telur kuning,

rissoto bubur, sarimi

Departemen 11 (Minuman

Rasa2)

pucukharum, sprite, pocari, tebs, teh kotak, calpico, ichi ocha,

nu greentea, uc1000, cafela, moontea, ale2, tehrio, floridina,

yakult, mirai ocha, okky koko, hydro coco, goodday, the jawa,

pulpy

Departemen 12 (Air

Mineral)aqua, aguaria, nestle, club

Departemen 13 (Susu) ultra uht, realgood, bendera, indomilk, dancow, anlene

Departemen 14 (Minuman

Sachet)

nutrisari, whitekoffi, coffemix, topkopi, s/msusu jahe,

sariwangi, torabika, tjatoet

Departemen 15 (Shampo) pantene, sunsilk, h&s sampo.

Departemen 16 (sabun) lifebuoy, biore, gatsby, dettol

Department Information

Departemen 17 (Tissue) paseo, nice, modern ctn bud

Departemen 18 (popok bayi) mamy poko

Departemen 19 (ice cream) Campina

Departemen 20 (Sandal) swallow, kiwi shoes

Departemen 21 (Perabot

Ruangan)stella, dahliakamp, sapulantai

Departemen 22 (peralatan

kantor)plastik, kertas kado, kertas asturo, isolasi,

Departemen 23 (Sikat Gigi

dan pelengkap)formula, xylitol, pepsoden

Departemen 24 (Sabun Cuci

Piring)ekonomi, sikat cuci, cling, lap

Departemen 25 (Sabun Cuci

Pakaian)daia, attack, rinso, soklin

Departemen 26 (Kosmetik )citra, ponds, medisoft, pixy, gillete, marina com powder,

larissa

Departemen 27 (Obat-

obatan)diapet,paramex, kaki3, biogesic

Departemen 28 (Rokok)

intro, djisamsoe, djarum spr filter, sampoerna, GG surya,

marlboro, LA Filter, u mild, dunhill, GG Signature, classmild,

djarum 76, lucky strike, a motionsampoerna

Departemen 29 (mainan) Detoys

4.2.3 Data Integration

Table 4.3 Data Integration

no Type

1 Department 11, Department 13, Department 7




5 Department 27, Department 13, Department 5, Department 3

6

Department 1, Department 4, Department 10, Department 14, Department 16,

Department 28

7 Department 19, Department 28




11


Department 9


















no Type

























53 Department 11, Department 18, Department 22, Department 14, Department 13




57 Department 16 fac non erfume, Department 20, Department 28


no Type






64


Department 11























87 Department 13, Department 10, Department 2, Department 11, Department 7 wfr,

no Type

Department 7











98


Department 17



4.2.4 Data Transformation

Table 4.4 Data Transformation Departement 1 - Departement19

NoDept

. 1

Dept.

2

Dept.

3

Dept

. 4

Dept.

5

Dept

. 6

Dept.

7

Dept

. 8

Dept.

9

Dept

. 10

Dept.

11

Dept.

12

Dept

. 13

Dept.

14

Dept

. 15

Dept.

16

Dept

. 17

Dept.

18

Dept.

19

1 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0

2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

3 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

5 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0

6 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0

7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

8 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0

9 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

10 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0

11 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0

12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

13 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

14 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0

15 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

16 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0

17 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0

NoDept

. 1

Dept.

2

Dept.

3

Dept

. 4

Dept.

5

Dept

. 6

Dept.

7

Dept

. 8

Dept.

9

Dept

. 10

Dept.

11

Dept.

12

Dept

. 13

Dept.

14

Dept

. 15

Dept.

16

Dept

. 17

Dept.

18

Dept.

19

18 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

19 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0

20 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

21 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0

22 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

23 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

24 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0

25 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

26 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

27 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0

28 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0

29 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

30 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

31 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

32 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

33 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0

34 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

35 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

36 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

NoDept

. 1

Dept.

2

Dept.

3

Dept

. 4

Dept.

5

Dept

. 6

Dept.

7

Dept

. 8

Dept.

9

Dept

. 10

Dept.

11

Dept.

12

Dept

. 13

Dept.

14

Dept

. 15

Dept.

16

Dept

. 17

Dept.

18

Dept.

19

37 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0

38 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0

39 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0

40 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

41 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0

42 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0

43 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

44 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

45 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1

46 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0

47 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0

48 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

49 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

50 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0

51 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

52 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

53 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 1 0

54 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

55 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0

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56 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0

57 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

58 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0

59 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

60 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0

61 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

62 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1

63 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

64 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0

65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

66 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0

67 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

68 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

69 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1

70 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0

71 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0

72 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0

73 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1

74 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0

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75 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0

76 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

77 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

78 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0

79 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0

80 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0

81 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0

82 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

83 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

84 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1

85 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0

86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

87 0 1 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0

88 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0

89 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0

90 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0

91 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0

92 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

93 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

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94 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

95 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

96 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

97 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

98 0 0 1 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 0

99 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0

100 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0

Table 4.5 Data Transformation Departement 20 – Departement 29

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1 0 0 0 0 0 0 0 0 0 0

2 0 0 0 0 0 0 0 0 1 0

3 0 0 0 0 0 0 0 0 1 1

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4 0 0 0 0 0 0 0 1 0 1

5 0 0 0 0 0 0 0 1 0 0

6 0 0 0 0 0 0 0 0 1 0

7 0 0 0 0 0 0 0 0 1 0

8 0 0 0 0 0 0 0 0 0 0

9 0 0 0 0 0 0 0 0 1 0

10 0 0 0 0 0 0 0 0 0 1

11 0 0 0 0 0 0 0 0 0 0

12 0 1 0 0 1 0 0 0 0 0

13 0 0 0 0 0 0 1 0 0 0

14 0 0 0 0 0 0 1 0 0 0

15 0 0 0 0 0 0 0 0 1 0

16 0 0 0 0 0 0 0 0 0 0

17 0 0 0 0 0 0 0 0 0 0

18 0 0 0 0 0 0 0 0 0 0

19 0 0 0 0 0 0 0 0 0 0

20 0 0 0 0 0 0 1 0 0 0

21 0 0 0 0 0 0 0 0 0 0

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22 0 0 0 0 0 0 0 0 1 0

23 0 0 0 0 0 0 0 0 0 0

24 0 0 1 0 0 0 0 0 0 0

25 0 0 0 0 0 0 0 0 1 0

26 1 0 0 0 1 0 0 0 0 0

27 0 0 0 0 0 0 0 0 0 0

28 0 0 0 0 0 0 0 0 0 0

29 0 0 0 0 0 0 0 0 0 0

30 0 0 0 0 0 0 0 0 0 0

31 0 0 0 0 0 0 0 0 1 0

32 0 0 0 0 0 0 0 1 0 0

33 0 0 0 0 0 0 0 0 0 0

34 0 0 0 0 0 0 0 0 0 0

35 0 0 0 0 0 0 0 0 0 0

36 0 0 0 0 1 0 0 0 0 0

37 0 0 0 0 0 0 0 0 0 0

38 0 0 0 0 0 0 0 0 0 0

39 0 0 0 0 0 0 0 0 0 0

40 0 0 0 0 0 0 1 0 0 0

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41 0 0 1 0 0 0 0 0 0 0

42 0 0 0 0 0 0 0 0 0 0

43 0 0 0 0 0 1 0 0 0 0

44 0 0 0 0 0 0 1 0 1 0

45 0 0 0 0 0 0 0 0 0 0

46 0 0 0 0 0 0 0 0 0 0

47 0 0 0 0 0 0 0 0 0 0

48 0 0 0 0 0 0 0 0 1 0

49 0 0 0 0 1 0 0 0 0 0

50 0 0 0 0 0 0 0 0 0 0

51 0 0 0 0 0 0 0 0 0 1

52 0 0 0 0 0 0 0 0 0 0

53 0 0 1 0 0 0 0 0 0 0

54 0 0 0 0 0 0 0 0 0 0

55 0 0 0 0 0 0 0 0 0 0

56 0 0 0 0 0 0 0 0 0 0

57 1 0 0 0 0 0 0 0 1 0

58 0 0 0 0 0 0 0 0 1 0

59 0 0 0 0 0 0 0 0 0 1

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60 0 0 0 0 0 0 0 0 1 0

61 0 0 0 0 0 0 0 0 0 0

62 0 0 0 0 0 0 0 0 0 0

63 0 0 0 0 0 0 0 1 0 0

64 0 0 0 0 0 0 0 0 1 0

65 0 1 0 0 0 0 0 0 0 0

66 0 0 0 0 0 0 0 0 0 0

67 0 0 0 0 0 0 0 0 0 0

68 0 0 0 0 0 1 0 0 0 0

69 0 0 0 0 0 0 0 0 0 0

70 0 0 0 0 0 0 0 0 0 0

71 0 0 0 0 0 0 0 0 0 0

72 0 0 0 0 0 0 0 0 0 0

73 0 0 0 0 0 0 0 0 0 0

74 0 0 0 0 0 0 0 0 0 0

75 0 0 0 0 0 0 0 0 1 0

76 0 0 0 0 0 1 1 0 0 0

77 0 0 0 0 0 0 0 0 1 0

78 0 0 0 0 0 0 0 0 1 0

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79 0 0 0 0 0 0 0 0 0 0

80 0 0 0 0 0 0 0 0 0 0

81 0 0 0 0 0 0 0 0 0 0

82 0 0 0 0 0 0 0 0 0 0

83 0 0 0 0 0 1 0 0 0 0

84 0 0 0 0 0 0 0 0 0 0

85 0 1 0 0 0 0 0 0 0 0

86 0 0 0 1 0 0 0 0 0 0

87 0 0 0 0 0 0 0 0 0 0

88 0 0 0 0 0 0 1 0 0 0

89 0 0 0 0 0 0 0 0 0 0

90 0 0 0 0 0 0 0 0 0 0

91 0 0 0 0 0 0 1 0 0 0

92 0 0 0 0 0 0 0 0 1 0

93 0 0 1 0 0 0 0 0 0 0

94 0 0 0 0 0 0 0 0 1 0

95 0 0 1 0 0 0 0 0 0 0

96 0 0 0 0 1 1 0 0 0 0

97 0 0 0 0 0 0 0 0 1 0

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98 0 0 0 0 0 0 0 0 0 0

99 0 0 0 0 0 0 0 0 0 0

100 0 0 0 0 0 0 0 0 0 0

4.2.5 Result of XL Rapid Miner

Table 4.6 Result of rapid miner

4.3 Analysis of the data processing result from the point of view of its consumer

behavior

From the data processing using Rapid Miner with minimum support and minimum

confidence of 0.2 by 0.1 RESULTS 6 rules as found in Table 4.2.5

1. Departement 11 with Departement 10

In this condition with score of Lift Ratio are 1.42 shows that every purchase

products at Department 11 will be buy department 10 with 5% value of support

and 19% value of confidance.







products at Department 7 will be buy department 6 with 5% value of support and

28% value of confidance.





5. Departemenet 10 with Departement 11




6. Department 6 with Departement 7


products at Department 6 will be buy department 7 with 5% value of support and

42% value of confidance.

4. 4 Solution recommendations

Figure 4.1.1 Indotoko Card Member

Based on the association rule result we found the biggest confidence which is 42%

and also added with 5% support value in every purchase products at Department 6

(believing tawar, week n roti, prambarusandwich, djawa bakery, madurasa, morita

selai, sari roti, kremer kental, perfecta kurma) will be buy department 7 (nyam2,

roma, malkis, trenz, tiniwinibiti, nissin frychip, nissin lemonia, timtam, nabati, kraft,

tango, monde, oreo, biskitop). And from the data of the receipts we found that in

department 6 most customers buy (djawa bakery, believing tawar, and prambaru

sandwich) which ratio 3:2:2 and from department 7 we found that (roma 5, malkis 3,

nabati 3). From the result, researcher make some deals which is in every purchase of

(djawa bakery, believing tawar, and prambaru sandwich, roma , malkis, nabati) that

reach until Rp. 100.000,00 will get 1 point. If the customer can collect until 5 point,

the customer will get 5% discount of purchasing using this member card.

CHAPTER V

CONCLUSION AND RECOMMENDATION

5.1 Conclusion

Based on rapid miner result of Association Rule calculation explain about the correlation

between (product) department 6 with department 7, because association rule told by

number, support is 0.05 and the confidence is 42 (in percent). So the solution is which is

in every purchase of (djawa bakery, believing tawar, and prambaru sandwich, roma ,

malkis, nabati) that reach until Rp. 100.000,00 will get 1 point. If the customer can collect

until 5 point, the customer will get 5% discount of purchasing using this member card.

5.2 Recommendation

5.2.1 For Retailer

We think based on our observation the retailer make a new card or member card

for easier the costumer to purchasing daily needs.

5.2.2 For Next Researcher

And for next researcher, the researcher already make a member card to make easier the next researcher to make another requirement to make a new member card.

Reference

Raeder, Chawla. (no year). “Market Basket Analysis with Networks”. University of Notre

Dame. USA

Gupta, Savi and Mamtora, Roopal (2014). ”A Survey on Association Rule Mining in

Market Basket Analysis”.ITM University, Gurgaon, INDIA

Annie, Kumar (2012) “Market Basket Analysis for a Supermarket based on Frequent

Itemset Mining”. Department of Computer Science, Government Arts College

Trichy, India

Miguel, Camanho, Joao Falcao “Mining Customer Loyalty Card Programs: The

Improvement of Service Levels Enabled byInnovative Segmentation and

Promotions Design.“ Faculdade de Engenharia da Universidade do Porto, Rua Dr.

Roberto Frias, 4200-465 Porto, Portugal.

ARMBA Associative Relations in Indotoko

Documents

personal retail

retail company

small retail

examples of retail

retail card member

retail sales of products

analysis of ar

member card