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CHAPTER I
INTRODUCTION
1.1 Description of the Problem
The development and comptetition in world trading throught free market economy and
information technology advance bring company in more opening tight competition level
to fulfill high customer expectation. The company must be apply good business standard
if they still want to compete and still has a market share. The competition in this business
cannot be separated from information technology that become the current issue today.
In several years, the development of retail business has a significant improvement,
especially in Yogyakarta. Every years many people move to Yogya because of their job
and business or the student who want to continue their study here. Based from that
situation this is the good chance to develop retail business in Yogyakarta. This is because
everyday people need to fulfill their daily needs, so the demand for retail industry in
Yogyakarta is still high. However their faced a new problem now. The new problem is
with recent market growing now, the competition between retail shop are tighter than
before. One of the example from competition is between Swalayan Kopma UGM and
Mirota Kampus that has closed location . Both of them are big retail store and has many
customer who visited and purchased their selling item everyday.
Swalayan Kopma UGM are located at Jalan Kaliurang Bulaksumur H-8 in front of
Vocational School Gadjah Mada University. Swalayan Kopma UGM is the retail with
supermarket format. Swalayan Kopma UGM are directed to become primary division
that will be provide basic needs for Gadjah Mada University student, such as college
equipment like stationary, tabloid, bulletin, journal, and magazine. They also have
grocery item to fulfill customer daily needs and Gadjahmada University accessories like
stickers, callendar, etc. Development of Swalayan Kopma UGM will be directed into
student shop center (Kopma UGM, 2015).
However, because of the competition with another retail shop they need to make a
strategy to defend in this retail business. From that a supermarket must understand what
their customer wants and needs to make them comfortable while shopping in that shop,
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especially to make customer choose their needed item easily. For the example, in placing
their shopping item that are composed in rack should fit to customer shopping pattern.
The problem that Swalayan Kopma UGM faced now is the layout of item placement
still not fit with the customer shopping pattern. This is because they still don’t see their
customer pattern who will purchased a product which placed near to each other and will
be purchase together. Based from that, very important to placed item that are suitable
with customer consumption pattern that actually can influence customer shopping tastes
and the selling for that product (Albion Research, 2007 in Lestari, 2009). The placement
item in a rack from a supermarket can be analyze from product selling transaction data ,
however Swalayan Kopma UGM still not using that transaction data to process become
knowledge that will be improve their business profit with the optimal placement of the
item.
One of the placement method that can analyze customer shopping behavior pattern is
Market Basket Analysis (MBA). This method is one of the method in data mining that
purpose to find the most selling products togeher based from transaction data. Analysis
pattern method of MBA shopping behavior used apriori algorithm, which will be used to
get association rule with pattern “if then”. That technique can be apply in very big datalike selling transaction data (Marsela dkk, 2004 in Bonai, 2011). With using data mining
technique will help people to can use MBA without using manual method.
Based from the explanation above, the objective of this research is design a new
layout solution for Swalayan Kopma UGM.
1.2
Problem Formulation
Problem formulation in this module MUST consists of:
1. How is the associative relations that happened between items in the Swalayan
Kopma UGM?
2.
How is the layout solutions for Swalayan Kopma UGM based on analysis of AR-
MBA?
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CHAPTER II
LITERATURE REVIEW
2.1. Deductive Study
2.1.1. Market Basket Analysis
Market Basket Analysis is a method or the often and most helpful technique for marketing
environment, this technique used association techniques from data mining. Association
techniques is a data mining technique for finding associative rules between combination
item (Eibe Frank et al, 2011). The objuective from this method is for determine which product will be bought together by customer at the same time.
Like an example how most likely a customer will buy bread together with milk. With
that knowledge, the owners of retail store or company can used that information to setting
a item layout so the most selling item together will be placed together in one area. Then,
the company that sell item online can used that information to setting a layout in their
store.
2.1.2. Association Rules
Association analysis or association rule is the technique in data mining to finding
associative rules between an item (Lutfi, 2009). Association rule has two steps, first search
combination which often happened in one item, then defining condition and result.
Like an example, associative rules from purchased analysis in a retail store is we can
know how big the possibility a customer will purchased bread together with milk. With
that knowledge, the owner of retail store can set the item layout or design market
campaign with using discount coupon for selected combination item.
The association rule that usually stated in:
{Bread, Margarine}→ {Milk} (Support = 40%, confidence =50%)
That rule means 50% from transaction in database which purchased bread and
margarine will purchased milk also. When 40% from all of the transaction in database has
three of that item. That also mean one customer that purchased bread and margarine has
50% possibility for purchased milk. This is significant enough because that represent
40% from all of transaction notes.
The customer behavior to implemented association analysis also need to understand if
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customer took one or batch of first item, this can be said a condition or left hand. Item or
batch of item that will be purchase next is result or right hand. For make an effective rule
in market basket analysis, there are three size that must be note such as “Support Score”,
“Confidence Score”, and “Improvement” or “Lift Score”.
a. Support
Support is a size that show percentage from basket where left hand and right hand both of
them are found together (Marakas, 2003). We can said that support is a buyer percentage
support who purchased item condition will purchase result item also from all of
transaction.
From that statement can be write as:
( Re) = (+)
x 100%
Formula for single selling item can be write as:
() =()
x 100%
The character of support is bidirectional, means support (A+B) is equal to support
(B+A).
b.
Confidence
This size are different from support because confidence is the possibility of apperance of
right hand item with knowing that left hand item already in shopping basket (Markas,
2003).
From the statement above the formulation is:
= (+)
() x 100%
Confidence won’t has many meaning if the item which is not sold are input in one
basket together with sold item. The characteristic of confidence is undirectional, so
confidence (A+B) are not definitely equal to confidence (B+A).
c. Improvement/Lift
After knowed the support that has bidirectional character and confidence that has
undirectional character, we want to know also is that true both of the rules are valid or
invalid. Improvement or lift is the number from combination of confidence divided with
support result, so the formula is:
= (+)
() x 100%
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The good rule is the rule that has improvement score more than 1,0 (Edi Suryanto, et
al. 2015).
2.2. Inductive Study
Research conducted by Heru Dewantara et al. (2013) with the title "Designing
Applications Data Mining with the Apriori Algorithm to Frequency Market Basket
Analysis on Sales Transaction Data”, researcher trying to made business strategy in
layout shopping item that adjusted to customer consumption pattern in Swalayan KPRI
Brawijaya University Malang. The method that researcher used is Market Basket
Analysis (MBA) with using transaction selling data during February 2013. The result of
this research is the MBA application prototype. Examination prototype did with
transaction minimum limitation (support) are 7 transaction an cofidence minimum are
5%. With that limitation, there are 11 association rules from MBA application. One of
association rule is if the customer purchased 1 kg sugar, Indofood Bumbu Racik Sayur
Sop 20gr, the customer will purchased Indofood Bumbu Racik Sayur Asem 20gr with
support score = 0,52% and confidence score 90,91% which will be highes confidence
score. Next process is categorized the used item as reference for making a solution
layout, so the conclusion is sugar will be placed near egg, instant seasoning, and oil; oil
will be placed near instant seasoning; egg will be placed near to rice and noodle also
beverage will be placed near to bread. With that, the layout of selling item can be adapted
with association rule and compatible with cusomer consumption pattern.
Research conducted by Olivia (2015) with the title "Perancangan Sistem Informasi
Data Mining Dengan Algoritma Apriori untuk Penentuan Layout Produk Pada PT. Metro
Makmur Nusantara”. PT. Merto Makmur Nusantara (Metro Supermarket) is one of many
recent growing retail business in Medan and the retail that oriented in food product and
non – food product. PT. Metro Makmur Nusantara already used Magic Retail
Information System (MARIS) to saves and process all of the selling data in company.
However, the program they applied cannot give an information that relationship with
taking a decision like market basket analysis from customer.
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To did that, they need to apply data mining method. Data mining is information
extract or important or interesting pattern from data in the unknown big base data, but
has a useful potential information. Apriopri Algorithm is the collected data algorithm
with association rule to determine an item combination association relationship.
Association Rule did with calculation mechanism support and confidence from an item
relationship. The result of this result is a data mining application that support company
management to know the most often purchased item together in one purchasing
transaction so the researcher can setting product layout. This will improved time
efficiency for customer to search and purchased item in supermarket.
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CHAPTER III
RESEARCH METHOD
3.1 Object of the Research
The object in this Association Rule – Market Basket Analysis research is in Swalayan
Kopma UGM at Jl. Kaliurang, H-8 Bulaksumur, Yogyakarta.
3.2 Collecting Data Method
The method of this research are took a group of sample that can represent population
with collected minimum 100 receipts, where the receipt are fulfill data pre-processing
condition without used failed transanction, not used one item only transaction data, and
also not use one department in one transaction data only. Researcher expected sample
which will be used in AR – MBA will be represent existing population, because this
analysis become good if sample are representative. We believe representative sample to
this population also can represent population.
3.3 Types of Data
3.3.1. Primary Data
Primary data in this research are customer shopping receipts which are collected through
collective process in location directly.
3.3.2. Secondary Data
Secondary data in this research is data that already proceed from primary data to data that
ready to further process. Secondary data in this research are buying data, transformation
data, and tabulation data and then ready to process with using software.
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3.4 Flowchart
Process to did this research are illustrated in flowchart below:
START
INPUT
DATA
Pre Processing Data:
1. Integration Data
2. Transformation Data
Tabulation Data
Association Rule
Solution Layout
FINISH
Figure 3.1 Flowchart AR – MBA
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The explanation in flowchart above:
a. Start the software, there are two software that researcher used in this report. There are
Microsoft Excel and Rapid Miner. The first step, start Microsoft Excel.
b. First input data recapitulation in Microsoft Excel. Data that will input in Microsoft
Excel are the item that customer purchased based from their receipt. Minimum item to
did this research are 2 different department in one receipt.
c.
After all of the data are recapitulated, the next process is integrate data from each
category. First, divided each item in several department. For determination of each
department are subjective, based from every researcher preferences to placed them in
each department. . When finished to integrated the data, after that transform data to become compatible form. After decided department for each item, copy the recapitulation
in the next sheet. Next click find in excel, click name of item, replace the name of the
item with typed the name of the department in that item, finally click replace all. Repeat
that step until one department are finished and repeat again to the next department until
all of department are finised. If there are 2 or more item in one department in one
transaction id, just choose one of them and delete the item in the same department.
d.
Process all of data become tabulation data, researcher need to change data department
to matrix binary. Binary number is the two character number 0 and 1. The reason to
transform the data is because Rapid Miner software just can process the output if the
input are made in matrix binary. First made a table that consist of departement and
transaction id. Next, give the number 1 for every department that are purchased in that
transaction id, and 0 if that department aren’t purchased. Finally the file are ready to
process using Rapid Miner software.
e.
Then, used Rapid Miner Software to got Association Rule and FP - Growth output.
First, open Rapid Miner Software. After that click new process. Then, click file, choose
import data, and choose import excel sheet. Next, search our excel data in computer then
click next. After choose the data, click next. The excel file will be opened in data import
wizard window choose matrix binary data and click next. In step three there is a preview
of tabulation data without name, just click next. For step 4 make sure all of box already
check, click next, save the data, and click finish.
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After the output are released when click finish, click main process. Drag file in main
process box. Search select attributes, then drag to main process box. Connect file and
select attributes, click select attributes and replace attribute filter by with subset, then
click select attributes menu in attributes, move all of department in right box. Search
numerical by binomial and remap binomials, then drag to main process box. Connect
select attributes with numerical by binomial and numerical to binomial with remap
binomial.After that replace negative value with 0 and positive value with 1. Search FP –
Growth and create association rules, then drag to main process box, after that connect
remap binomials to FP Growth and FP – Growth (fre) to create association rules, next
connect FP – Growth (exa) to first res and create association rules (rul) to second res.
After that, click FP – Growth fill the value of min support and click create association
rule, then replace minimum confidence value. The value both of min support and min
confidence are subjective based from researcher preference. Finally, click Run and the
output of the process will be released. The association rule output is possibility if the
item in one department are purchased together. FP Growth output is the most selling
item.
f. Finally, based from association rule and fp growth output we can read the conclusion
of both output. After that, made a solution layout for this case. For example, the most
selling item must be placed near to cashier and placed together with the most possibility
item that will be purchased together with the most selling item. Researcher using
Microsoft Visio to made a layout solution in this case.
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CHAPTER IV
RESULT AND DISCUSSION
4.1. Initial condition of the research object
Swalayan Kopma UGM is the retail shop that has many visitor enough everyday,
especially in school day when student has class in their university. Because most of
this shop customer are student, most of the selling item are for student needs, like
stationary and item for student of Gadjahmada University. Then this shop has many
item besides stationary and UGM student item like tabloid, magazine, newspaper, and
even daily needs.
The case study now are about layout in Swalayan Kopma UGM. Researcher went
to Swalayan Kopma UGM to collected shopping receipt. After that researcher analyze
if the layout already compatible or not based from what the most purchased item by
customer. From all of that item they are selling, the layout of Swalayan Kopma UGM
like the picture below.
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Figure 4.1 Initial Layout of Swalayan Kopma UGM
In this layout, the item which placed closed to cashier are candies, batteries and
bread. After that shampoo, cosmetic, and cleanser are placed closed each other. Snack,
instant noodle, and household equipment are placed closed each other. Beverages are
placed close to raincoat. Raincoat is placed close to clothes and accessories. Sachet
drink, biscuit and instant seasoning are placed close to each other.
4.2. Rapid Miner Output
After collected all of the receipt, researcher need to recapitulated it and made several
department based from all of item which customer bought. Then, made a
preprocessing data and next made transformation data in Microsoft Excel. Finally,
input excel file to software Rapid Miner, and the output will be calculated like picture
and table below.
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Figure 4.2 Main Process
After made main process, click run and the output will be shown like table below.
FP – Growth is the table that shown the most purchased item by customer.
Table 4.1 FP - Growth
Size Support Item 1 Item 2
1 0.630 DEPT.12
1 0.200 DEPT.6
1 0.190 DEPT.23
1 0.170 DEPT.7
1 0.150 DEPT.14
1 0.140 DEPT.25
1 0.140 DEPT.16
1 0.140 DEPT.13
1 0.130 DEPT.17
1 0.130 DEPT.11
1 0.100 DEPT.18
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Size Support Item 1 Item 2
1 0.090 DEPT.4
1 0.080 DEPT.5
1 0.080 DEPT.22
1 0.070 DEPT.9
1 0.050 DEPT.3
1 0.050 DEPT.20
2 0.140 DEPT.12 DEPT.6
2 0.070 DEPT.12 DEPT.23
2 0.070 DEPT.12 DEPT.7
2 0.080 DEPT.12 DEPT.14
2 0.080 DEPT.12 DEPT.25
2 0.080 DEPT.12 DEPT.16
2 0.060 DEPT.12 DEPT.13
2 0.070 DEPT.12 DEPT.11
2 0.060 DEPT.12 DEPT.4
2 0.080 DEPT.12 DEPT.5
Association Rules is table that describe if “Item B” will be choose if “Item A” are
bought. The table of association rules are like the table below.
Table 4.2 Association Rules
No Premises Conclusion Support Confidence La - Place Gain p-s Lift Confiction
1 DEPT.12 DEPT.23 0.07 0.11 0.656 -1.19 -0.049 0.584 0.911
2 DEPT.12 DEPT.7 0.07 0.11 0.656 -1.19 -0.037 0.653 0.933
3 DEPT.12 DEPT.11 0.07 0.11 0.656 -1.19 -0.012 0.854 0.978
4 DEPT.12 DEPT.14 0.08 0.126 0.662 -1.18 -0.015 0.846 0.973
5 DEPT.12 DEPT.25 0.08 0.126 0.662 -1.18 -0.008 0.907 0.985
6 DEPT.12 DEPT.16 0.08 0.126 0.662 -1.18 -0.008 0.907 0.985
7 DEPT.12 DEPT.5 0.08 0.126 0.662 -1.18 0.0296 1.587 1.053
8 DEPT.12 DEPT.6 0.14 0.222 0.699 -1.12 0.0140 1.111 1.028
9 DEPT.23 DEPT.12 0.07 0.368 0.899 -0.31 -0.049 0.584 0.585
10 DEPT.7 DEPT.12 0.07 0.411 0.914 -0.27 -0.037 0.653 0.629
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No Premises Conclusion Support Confidence La - Place Gain p-s Lift Confiction
11 DEPT.13 DEPT.12 0.06 0.428 0.929 -0.220 -0.028 0.680 0.647
12 DEPT.14 DEPT.12 0.08 0.533 0.939 -0.219 -0.014 0.846 0.792
13 DEPT.11 DEPT.12 0.07 0.538 0.946 -0.19 -0.012 0.854 0.801
14 DEPT.25 DEPT.12 0.08 0.571 0.947 -0.2 -0.008 0.907 0.863
15 DEPT.16 DEPT.12 0.08 0.571 0.947 -0.2 -0.008 0.907 0.863
16 DEPT.4 DEPT.12 0.06 0.666 0.972 -0.12 0.0033 1.058 1.11
17 DEPT.6 DEPT.12 0.14 0.7 0.950 -0.26 0.0140 1.111 1.233
18 DEPT.5 DEPT.12 0.08 1.0 1.0 -0.08 0.0296 1.587 Infinity
4.3.
Analysis of the data processing result from the point of view of its consumer
behavior
From the data that already got from 100 receipt in Swalayan Kopma UGM Jl.
Kaliurang Bulaksumur, researcher did a researched to know asosiation relationship
between item that related to customer behavior. The item which bought by the
customer are the heterogen item that classified to be several department. After
processed the data, there are 25 department such as newspaper, staple food, sugar,
snack, bread, biscuit, candy, ice cream, instant noodle, spice, drink sachet, beverages,
sanitary napkin and tissue, clothes and accessories, shoe polish stationary, cosmetics,
tooth brush and paste, shampoo, soap, cleaner, household appliance, medicine, baby
equipment, and ciggarette and crickets.
Based from calculation using Rapid Miner software, the result in Frequent Pattern
Growth ( F-P Growth) table are Department 12(beverage) has 0,63 support,
department 6 (biscuit) has 0,20 support, department 23(medicine) has 0,19 support
,department 7 (candies) has 0,17 support, department 14 (clothes and accessories) has0,15 support score, department 25 (ciggerattes and crickets) has 0,14 support score,
department 16 (stationary) has 0,14 support, department 13 (sanitary napkin and
tissue) has 0,14, department 17 (cosmeticts) has 0,13 support score, department 11
(sachet drink) has 0,13 support, department 18 (tooth brush and paste) has 0,10
support, department 4 (snack) has 0,09 support, department 5 (bread) has 0,08
support, department 22 (houshold appliaces) has 0,08 support, departement 9 (instant
noodle) has 0,07 support, department 3 (sugar) has 0,05 support, and department 20(soap) has 0,05 support. If there are 2 item which frequent be choosen by the
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customer, there are department 12 and department 6 which has 0,14 support,
department 12 and department 23 has 0,07 support, department 12 and department 7
has 0,07 support, department 12 and department 14 has 0,08 support, department 12
and 25 has 0,08 support, department 12 and department 16 has 0,08 support,
department 12 and department 13 has 0,06 support, department 12 and department 11
has 0,07 support, department 12 and department 4 has 0,06 support, and department
12 and 5 has 0,08 support. In FP- Growth table result, researcher used 0,2 as the
minimum support for this process.
For Association Rule table, researcher used 0,1% minimum confident. The result
of the association rule are:
a.
Rule 1
If customer purchased item in department 12, customer must be purchased item in
department 23 with 0,07 support score and 0,11 confident score.
b. Rule 2
If customer purchased item in department 12, customer must be purchased item in
department 7 with 0,07 support score and 0,11 confident score.
c. Rule 3
If customer purchased item in department 12, customer must be purchased item in
department 11 with 0,07 support score and 0,11 confident score.
d. Rule 4
If customer purchased item in department 12, customer must be purchased item in
department 14 with 0,08 support score and 0,127 confident score.
e. Rule 5
If customer purchased item in department 12, customer must be purchased item in
department 25 with 0,08 support score and 0,127 confident score.
f.
Rule 6
If customer purchased item in department 12, customer must be purchased item in
department 16 with 0,08 support score and 0,127 confident score.
g.
Rule 7
If customer purchased item in department 12, customer must be purchased item in
department 5 with 0,08 support score and 0,127 confident score.
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h.
Rule 8
If customer purchased item in department 12, customer must be purchased item in
department 6 with 0,14 support score and 0,14 confident score.
i.
Rule 9
If customer purchased item in department 23, customer must be purchased item in
department 12 with 0,07 support score and 0,368 confident score.
j. Rule 10
If customer purchased item in department 7, customer must be purchased item in
department 23 with 0,07 support score and 0,412 confident score.
k. Rule 11
If customer purchased item in department 13, customer must be purchased item in
department 12 with 0,06 support score and 0,429 confident score.
l. Rule 12
If customer purchased item in department 14, customer must be purchased item in
department 12 with 0,08 support score and 0,533 confident score.
m. Rule 13
If customer purchased item in department 11, customer must be purchased item in
department 12 with 0,07 support score and 0,538 confident score.
n.
Rule 14
If customer purchased item in department 25, customer must be purchased item in
department 12 with 0,08 support score and 0,571 confident score.
o.
Rule 15
If customer purchased item in department 16, customer must be purchased item in
department 12 with 0,06 support score and 0,667 confident score.
p.
Rule 16
If customer purchased item in department 4, customer must be purchased item in
department 12 with 0,06 support score and 0,667 confident score.
q. Rule 17
If customer purchased item in department 6, customer must be purchased item in
department 12 with 0,14 support score and 0,7 confident score.
r. Rule 18
If customer purchased item in department 5, customer must be purchased item in
department 12 with 0,08 support score and 1 confident score.
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From the result both of FP – Growth and Association rule, the conclusion are the
most purchase item by cutomers are the item from department 12 (beverages), and in
association rule all of item from each department must be pairing with item in 12
department when customer shopping in Swalayan Kopma UGM.
4.4. Solution recommendations based on the associations rule result
After found result of the Frequent Pattern Growth (FP – Growth) and Association rule,
researcher can found the solution layout where:
a.
The most purchased item (department 12) must be close to cashier. This is
because cashier are closed to door, so this will make customer take this item easier
than to placed it far enough from cashier like the initial layout.
b. The department which always choose after department 12 must be placed closed
to department 12. This will make customer can search and take the item easily. From
that result, the item that must be placed closed department 12 are department 23,
department 7, department 11, department 14, department 25, department 16,
department 5, and department 6.
c. The item which placed near to cashier but not absolutely need to placed close to
department 12 are department 4, department 13, department 17, department 18,
department 4, department 22, department 9, department 3, and department 20.
Based from that result, the layout solution that researcher recommend to Swalayan
Kopma UGM, are like the figure below.
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Figure 4.3 Solution Layout for Swalayan Kopma UGM
In this solution layout, researcher purposely made the layout like this based from
the result of FP – Growth and Association Rules. Researcher hope with this new
layout, customer can search the item easily especially for the item which are
purchased together with the most selling item. Then the item which are placed near to
cashier can be found easily by the customer. Finally, because of that simplicity can
improve customer satisfaction, and improve business profit.
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CHAPTER V
CONCLUSION AND RECOMMENDATION
5.1 Conclusion
From all result of calculation in Rapid Miner software, researcher conclude that
a. From FP – Growth result the most selling item are from department 12 (beverages).
Then in Association Rule output, when customer purchased item in department 12, they
must be purchased item from department 23, department 7, department 11, department
14, department 25, department 16, department 5, and department 6.
b.
Based from the result of the Association Rule and FP - Growth, the layout solution for
this case are researcher placed the most selling item near to cashier and the item that will
be purchased together with most selling item will be placed closed to each other.
5.2 Recommendation
5.2.1.
For Future Research
Recommendation for future research, based from research limitation. Limitation in this
research
a. Limitation of the population sample, researcher just took 100 sample receipt in
Swalayan Kopma UGM, in the future researcher recommend to take more sample so the
improve support value so the item appear frequently.
b. Limitation of collecting data method, researcher just took a receipt to made this report.
For the future researcher recommend to add collecting data method, like interview to the
customer or the expert in Swalayan Kopma UGM.
5.2.1.
For Swalayan Kopma UGM
Researcher recommend Swalayan Kopma UGM to repair their initial layout with placed
the most selling item near to the cashier followed by the item which are always
purchased together with the most selling item.
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REFERENCE
Dewantara, Heru et al. 2013. Designing applications data mining with the apriori
algorithm to frequency market basket analysis on sales transaction data
(Case Study in Supermarket KPRI University of Brawijaya).Malang:
University of Brawijaya.
Olivia. 2015. Perancangan sistem informasi data mining dengan algoritma apriori
untuk penentuan layout produk pada pt. Metro makmur nusantara. Medan:
STMIK TIME
Suryanto, Edi et al. Implementasi customer relationship management dengan
market basket analysis pada toko buku online (studi kasus: toko
buku toga mas).
Kopma UGM. 2015. Divisi usaha. (online): http:kopmaugm.com/divisi-usaha(June 21st 2015 )
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ATTACHMENT
7/23/2019 Laporan ar mba ip 4
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ANALYSIS DECISION AND DATA MINING ASSOCIATION RULE–MARKET BASKET ANALYSIS (AR-MBA)
IP – 4 GROUP 4
1.
Syarifah Nabila AR
ASSISTANT DM - 40
DATA MINING LABORATORY
INDUSTRIAL ENGINEERING DEPARTMENT
INDUSTRIAL TECHNOLOGY FACULTY
ISLAMIC UNIVERSITY OF INDONESIA
2015
Scoring Criteria Max
Format 10
Content 50
Analysis 40
TOTAL 100
Yogyakarta,June 23rd 2015
Assistant
(Doni Hikmawan)
PRACTICUM REPORT