clustering method

CHAPTER I

INTRODUCTION

1.1 Description of the Problem

In Indonesia, especially in Jogjakarta there are many restaurant that offer many kind

of foods which is owned by personal or group. One example of the personal is SGPC Bu

Wiryo. SGPC Bu Wiryo established in 1959, by married couples Dario and Suyati who have

family names Wiryo soenarto. At the time it was set up stalls located on the east Auditorium

of UGM. In 1994 the location of the shop is shifted to Jalan Agro CT VIII, Sleman, north

Sewers Mataram. Pecel, one of the typical culinary Jogja. Has the character of boiled

vegetables, usually consisting of bean sprouts, kale, spinach, beans and other spices soaked

typical pecel made from beans. SGPC itself is an abbreviation of the word Sego Pecel (Rice

Pecel). Seasoning dough pecel fitting a mainstay of places to eat this one, spices peanut indeed

serving sweet and spicy flavors that are perfectly integrated so as to make its own distinctive

flavor. SGPC Bu Wiryo still maintaining a simple construction, long tables and benches made

of wood filled the stalls lined up, more and gives a strong impression that the SGPC Bu Wiryo

understated and remains consistent inherit traditional culture. In addition there is also a music

group that is ready to entertain you when to stop in this shop by playing pop songs and folk

songs. As the name implies, the main menu is of course sego stalls and pecel (rice and pecel).

Not sego pecel that we usually encounter, but sego pecel specials with special seasoning

dressing anyway. We may be surprised, because it was not until 2-3 minutes menu we ordered

ready to eat. Speed and alacrity waiter is preferred to appreciate the arrival of visitors. For the

price we need not worry, because for a very affordable price.

Modernity with technological advances will result in a very tight competition to

acquire and retain customers. Consumption patterns and lifestyles of customers demanding the

company is able to provide a quality service and it will related with costumer behavior.

Consumer behavior considers the many reasons why personal, situational, psychological, and

social-people shop for products, buy and use them, and then dispose of them. Quality is the

starting point in taking market share so that the level of satisfaction is not only maintained but

also need to be improved to deal with increasingly fierce competition.

Studying people’s buying habits isn’t just for big companies, though. Even small

businesses and entrepreneurs can study the behavior of their customers with great success. In

every company or firm that has a product, their goal is about customer satisfaction. Customer

satisfaction is a key factor in formation of customer’s desire for the future purchase . So if

customer satisfied with our product, even service or physical, customer will come and

purchased the product to our company more and more. Some businesses, including a growing

number of startups, are using blogs and social networking Web sites to gather information

about their customers at a low cost.

Generally, Customers is differing with the consumer, can be regarded as a customer if

the person starts getting used to purchase products or services offered by a business entity.

Habits can be built through repeated purchases within a certain period, if within a certain time

does not make the purchase again then that person cannot be said as a customer but as a buyer

or consumer. Customer satisfaction will also be met if the service provider is able to examine

the specific criteria of each service to be provided to the consumer (Anung Pramudyo, 2012).

Economic progress and education, has resulted in wider choices. With today's changing

lifestyles, consumers tend to eat in a place with considering cleanliness, service, facilities,

favor, and comfortable ambiance.

.In order to know whether customer is satisfied or not, we conduct observation with

Cluster Sampling method. Clustering is the task of grouping a set of objects in such a way that

objects in the same group (called a cluster) are more similar (in some sense or another) to each

other than to those in other groups (clusters). It is a main task of exploratory data mining, and

a common technique for statistical data analysis. Cluster also one of multivariate techniques

used in data mining, which has objective to identify a set of object with certain similar

characteristics that could be separated with the other groups of objects, such that those within

each group are more closely related (homogeny) to one another than object assigned to

different group.

Based on the Journal of Dyah, Herdiana, the different with the research before is in

analisys of the cluster is from the characteristic of the object which is another Restaurant that

has capability to considering the quality of the product that can be implement in the RM

Warung Kuning Surabaya. In the research before also mentioned about the method of analysis

MANOVA (Multivariate Anova) that has objectives to test the similar vector from the

average of dependent variable in a group.

1.2 Problem Formulation

In this study the problem is formulated as follows:

1. What indicators that significantly affect the observed variables in this study especially

in SGPC Bu Wiryo?

2. How much and how the characteristics of each cluster is formed based on the quality

questionnaire?

1.3 Research Objectives

The research objective of this research is:

1. To know the indicators that significantly affects the observed variables (adjusted with a

variable in the case study, respectively).

2. To determine the number and shape of the characteristics of each cluster is formed.

CHAPTER II

LITERATURE REVIEW

2.1 Deductive Study

Cluster analysis or clustering is the task of grouping a set of objects in such a way that

objects in the same group (called a cluster) are more similar (in some sense or another) to each

other than to those in other groups (clusters). Cluster analysis itself is not one

specific algorithm, but the general task to be solved. It can be achieved by various algorithms

that differ significantly in their notion of what constitutes a cluster and how to efficiently find

them. Number of groups that could be identified depends on the quantity and variation of

object data. The aim of forming clusters is for further analysis and interpretation in accordance

to the objective of the research. Cluster solution, in a whole, depends on the variables, which

are used as the basic to measure similarity. Addition or reduction to relevant variables can

influence the substance as a result of cluster analysis.

There are several steps for conducting cluster analysis that are;

1) Objectives of cluster analysis.

2) Research design in cluster analysis.

3) Assumption in cluster analysis.

4) Process to get the clusters and measuring the overall feasibility (Overall Fit).

5) Interpretation toward cluster.

6) Validation process and making cluster’s profile.

There are two techniques in cluster method, Hierarchical and Non-hierarchical method.

Hierarchical method is a clustering technique, which form hierarchy construction or based on

certain stage like tree structure (match structure). The grouping process was done stage by

stage.

In data mining, hierarchical clustering is a method of cluster analysis which seeks to build

a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types:

Agglomerative: This is a "bottom up" approach: each observation starts in its own

cluster, and pairs of clusters are merged as one moves up the hierarchy.

Divisive: This is a "top down" approach: all observations start in one cluster, and splits

are performed recursively as one moves down the hierarchy.

Different with the hierarchical method, the procedure of non-hierarchical method (K-

means clustering) is started with determining a desirable number of initial clusters and then the

objects are joined to those clusters. There several procedure in non-hierarchical method that

are;

1) Sequential threshold procedure

2) Parallel threshold procedure

3) Optimizing

In the present study observed object is the quality of the Restaurant. The location of restaurant

is located Jalan Agro CT VIII, Klebengan, Sleman, DIY named SGPC Bu Wiryo. Located in

the strategic place make SGPC Bu Wiryo became the main destination for them who want buy

some product there. With today's changing lifestyles, consumers tend to eat in a place with

considering cleanliness, service, facilities, favor, and comfortable ambiance. So we include

those variables to differentiate each cluster in order to get the information that needed from

customer.

Customer buying resistance one factor that is essential for a company to beable to

continue to run their business , especially if the company is engaged in the service.

Competition restaurant and catering business becomes increasingly tight , this can be seen

with many emerging new restaurant and catering that offers features and uniqueness of each

and causing consumers more selective in choosing the restaurant and catering as desired.

Restaurant and catering which has a long-standing need to be able to compete, so as not to lose

customers . It is therefore necessary to design an enterprise service that customers purchase

the right so that resistance can be increased , which in turn will positively impact the

development and progress of the company.

According to (Berry 2006) there is a well-developed literature in IO on product quality in

market equilibrium that we can draw on to motivate the empirical illustrations in this paper.

We informally review and illustrate that theory here; To address the issues in this paper, we

begin with a simple vertical quality model. Suppose that the utility to consumer i of product j

is.

where δj is product quality and pj is price. Note that we have assumed away income effects,

and utility is measured in dollars, so that θi is the consumer’s willingness-to-pay for quality.

We assume that θi is distributed on the interval (0,∞) so that there are some consumers with

arbitrary high θ’s who will pay for an increase in quality to any level. We also assume that

there is a “outside” good of quality zero, available at a price of zero (which is the marginal

cost of a zero quality good.)

2.2 Inductive Study

Based on the Journal of Dyah, Herdiana, the different with the research before is in

analisys of the cluster is from the characteristic of the object which is another Restaurant that

has capability to considering the quality of the product that can be implement in the RM

Warung Kuning Surabaya. In the research before also mentioned about the method of analysis

MANOVA (Multivariate Anova) that has objectives to test the similar vector from the

average of dependent variable in a group. While our research is using hierarchical clustering

algorithm and the analysis is from the customer who willing to visit the resturant. In

hierarchical clustering algorithm, which clusters are in the upper level (intermediate level)

form other clusters can be obtained by combining two clusters are at the level below it. The

overall results of the hierarchical clustering algorithm can be described graphically as at tree.

Called a dendrogram (Tan, 2006). Dendrogram itself is a tree diagram frequently used to

illustrate the arrangement of the clusters produced by hierarchical clustering. Dendrogram are

often used in computational biology to illustrate the clustering of genes or samples. The data

that can be use in dendrogram from our research is come from the cluster of the customers

which tend to eat in RM SGPC Bu Wiryo recently. The figure of these methods is:

Figure 2.1 Dendrogram Tree Diagram Interface

CHAPTER III

RESEARCH METHOD

3.1 Object of the Research

In the present study observed object is the quality of the Restaurant. The location of

restaurant is located Jalan Agro CT VIII, Klebengan, Sleman, DIY named SGPC Bu Wiryo.

Located in the strategic place make SGPC Bu Wiryo became the main destination for them

who want buy some product there.

3.2 Collecting Data Method

The method used for data collection in this study was the questionnaire. Questionnaires

were administered in the form of multiple choices regarding personal data of respondents

rating scale and the variable quality of some of SGPC Bu Wiryo in Jalan Agro CT VIII,

Klebengan, Sleman, DIY. Questionnaire was filled in by the respondent, the person who

eating in SGPC Bu Wiryo.

3.3 Data Selection

a. Primary

Collecting data method can get directly with spreading the questionnaire to the customers.

Method that used by researcher is Cluster. From observation we set the RM SGPC Bu

Wiryo as the observation place and directly research at there. From the questionnaire, we

get the result of customers profile which is visit RM. SGPC Bu Wiryo.

b. Secondary

This secondary collecting data method is using literature from the Cluster practicum

module and journal. Then we took a reason of choosing attributes of quality dimension

based on the journal of (Susanti, 2013).

3.5 Flowchart

Herewith the flowchart process based on our research in the RM SGPC Bu Wiryo:

Figure 3.1 Flowchart process of the research

CHAPTER IV

RESULT AND DISCUSSION

4.1. Selection of Indicator Variables

In this case the researcher talk about quality in RM. SGPC Bu Wiryo. From the journal, we

got some variables, there are:

1. Cleanliness

Quality of cleanliness will encourage consumers to establish a close relationship with

the restaurant. Consumer satisfaction will ultimately create consumer loyalty to a

company that provides a satisfactory quality of their facilities (Susanti, 2013)

2. Service

Because of the service can improve customer satisfaction. Quality of service is included

in the inseparability characteristics in services, therefore one way of improving the

quality of the waitress is through recruitment through better selection and training of

employees. (Susanti, 2013)

3. Facilities

It also affects consumers in layout of the room to be interested in a restaurant for adding

consumers satisfaction when all consumers eating their food (Susanti, 2013)

4. Favor.

It also affects consumers to be interested in a restaurant and also influence consumer

behavior restaurants (Susanti, 2013)

5. Comfortable ambiance

Ambience can be seen from how the interior design and exterior. Because the

restaurant RM. SGPC Bu Wiryo positioned as a family restaurant, the ambiance which

are presented in the matter is a family atmosphere (Susanti, 2013)

4.2. Summary of Questionnaire Results

Table 4.2.1 Summary of Questionnaire

name Gender Age Job Intensities Cleanliness Service Facilities Favor

Comfortable

ambiance

partiyah Female

≥ 36 years

old Entrepreneurship Often agree agree not agree

strongly

agree

strongly

agree

desti Female

26-35 years

old Other job

Several

times agree

not

agree not agree agree

strongly

agree

tugiyati Female

≥ 36 years

old

Office

employees Often agree

not

agree

strongly

agree

strongly

agree

strongly

agree

cecil Female

26-35 years

old

Government

employees

Several

times agree

strongly

agree

strongly

agree

strongly

agree

strongly

agree

dila Female

26-35 years

old

Office

employees

Several

times

strongly

agree

strongly

agree

strongly

agree

strongly

agree

strongly

agree

retno Female ≤25 years old Other job Seldom agree

not

agree agree

strongly

agree

strongly

agree

amoy Female ≤25 years old Other job

Several

times

strongly

agree

not

agree not agree agree agree

marwah Female

≥ 36 years

old Entrepreneurship

Several

times not agree agree

strongly

agree

strongly

agree

strongly

agree

vicky Female 26-35 years Office Often agree strongly agree strongly strongly


Comfortable

ambiance

old employees agree agree agree

dina Female ≤25 years old Other job Often agree

not


strongly

agree

maya Female

26-35 years

old Entrepreneurship Often not agree agree agree

strongly

agree

strongly

agree

yuni Female

≥ 36 years

old Entrepreneurship Seldom

strongly

agree agree agree

strongly

agree

strongly

agree

annisa Female ≤25 years old Other job Often agree agree

strongly

agree

strongly

agree agree

reza Male ≤25 years old Other job

Several

times not agree agree agree

strongly

agree

strongly

agree

lina Female

26-35 years

old

Government

employees

Several

times agree

strongly

agree

strongly

agree agree agree

harwanto Male ≤25 years old Entrepreneurship

Several

times agree agree not agree agree

strongly

agree

jati Male ≤25 years old Entrepreneurship

Several

times agree

not

agree agree agree

strongly

agree

wijang Male

26-35 years

old Entrepreneurship Often agree agree not agree agree

strongly

agree

singgih Male 26-35 years Entrepreneurship Often agree not agree strongly strongly


Comfortable

ambiance

old agree agree agree

yudha Male

26-35 years

old

Office

employees Seldom agree

not

agree agree

strongly

agree

strongly

agree

andreas Male ≤25 years old Other job Seldom agree agree agree agree agree

tommy Male ≤25 years old

Office


not

agree agree

strongly

agree

strongly

agree

sulistyowati Male

≥ 36 years

old

Office

employees Often agree

strongly

agree

strongly

agree

strongly

agree

strongly

agree

fantri Male

26-35 years

old Entrepreneurship Seldom agree

not

agree agree

strongly

agree

strongly

agree

wawan Male

≥ 36 years

old Entrepreneurship Often agree

not

agree agree

strongly

agree

strongly

agree

danto Male

26-35 years

old

Government


not


strongly

agree

egi Male

26-35 years

old

Office


strongly

agree

strongly

agree

strongly

agree

strongly

agree

dalmindi Male

26-35 years

old Entrepreneurship Seldom

strongly

agree agree

strongly

agree

strongly

agree

strongly

agree

sugeng Male 26-35 years Office Often agree not not agree agree agree


Comfortable

ambiance

old employees agree

garindra Male

≥ 36 years

old Other job Seldom agree agree

strongly

agree

strongly

agree

strongly

agree

rizky Male ≤25 years old Other job Seldom agree

strongly

agree agree

strongly

agree

strongly

agree

hardi Male

≥ 36 years

old Entrepreneurship Often agree agree

strongly

agree agree

strongly

agree

budianto Male ≤25 years old Other job Seldom agree agree agree agree agree

baerozi Male

26-35 years

old

Office

employees

Several

times agree

not

agree agree

strongly

agree not agree

sharno Male

26-35 years

old

Government

employees

Several

times agree

not

agree agree

strongly

agree

strongly

agree

yanto Male

26-35 years

old

Government

employees

Several

times agree agree not agree

strongly

agree

strongly

agree

kusno Male

26-35 years

old

Government

employees

Several

times agree

strongly

agree

strongly

agree

strongly

agree

strongly

agree

anggarista Male

≥ 36 years

old

Government

employees Often agree agree

strongly

agree

strongly

agree not agree

dicky Male 26-35 years Government Several agree agree strongly strongly strongly


Comfortable

ambiance

old employees times agree agree agree

tito Male

≥ 36 years

old

Government

employees Often agree agree

strongly

agree

strongly

agree

strongly

agree

ikhsanudin Male

26-35 years

old

Government

employees

Several

times agree

not

agree not agree

strongly

agree

strongly

agree

Gender : 1. Male Variable : 1. Strongly agree

2. Female 2. Agree

Age : 1. ≤ 25 years old 3. Not agree

2. 26-35 years old 4. Strongly disagree

3. ≥ 36 years old

Job : 1. Entrepreneurship

2. Office employees

3. Government employees

4. Other Job

Intensities : 1. Seldom

2. Several times

3. Often

4.3. Output and Analysis of Dendrogram

Figure 4.1 Single Linkage Dendrogram

How to read and divide the name to entire cluster is cutting off in some distance. In the

distance of five, we cut then we get who will get into cluster 1 and 2. Then in the distance of

15 we get who will get into cluster 3. Then again, in the distance of 15 we get who will get

into cluster 4. Then in the distance of 25, we get who will get into cluster 5.

4.1. Variable Crosstab Result

4.4.1 Cleanliness

4.2 Cleanliness Crosstab

cluster

Total1.00 2.00 3.00 4.00 5.00

Cleanliness Extremely

Agree

Count 0 2 0 2 0 4

% of

Total0.0% 4.9% 0.0% 4.9% 0.0% 9.8%

Agree Count 3 4 13 10 4 34

% of

Total7.3% 9.8% 31.7% 24.4% 9.8% 82.9%

Not Agree Count 0 3 0 0 0 3

% of

Total0.0% 7.3% 0.0% 0.0% 0.0% 7.3%

Total Count 3 9 13 12 4 41

% of

Total7.3% 22.0% 31.7% 29.3% 9.8% 100.0%

From the result of crosstab of Cleanliness can be described as in cluster 1, we have 0

extremely agree with a percentage 0%, 3 agree with a percentage 7.3%, and 0 not agree with a

percentage 0.0%.

In cluster 2, we have 2 extremely agree with a percentage 4.9%, 4 agree with a

percentage 9.8%, and 3 not agree with a percentage 7.3%.

In cluster 3, we have 0 extremely agree with a percentage 0%, 13 agree with a






4.4.2 Service

4.3 Service Crosstab

cluster

Total1.00 2.00 3.00 4.00 5.00

Service Extremely

Agree

Count 0 0 6 1 1 8

% of

Total0.0% 0.0% 14.6% 2.4% 2.4% 19.5%

Agree Count 3 8 0 4 2 17

% of

Total7.3% 19.5% 0.0% 9.8% 4.9% 41.5%


% of

Total0.0% 2.4% 17.1% 17.1% 2.4% 39.0%

Total Count 3 9 13 12 4 41

% of

Total7.3% 22.0% 31.7% 29.3% 9.8% 100.0%






percentage 0%, and 7 not agree with a percentage 17.1%.





4.4.3 Facilities

4.4 Facilities Crosstab

Cluster

Total1.00 2.00 3.00 4.00 5.00

Facilities Extremely

Agree

Count 3 6 4 1 1 15

% of

Total7.3% 14.6% 9.8% 2.4% 2.4% 36.6%


% of

Total0.0% 7.3% 22.0% 2.4% 7.3% 39.0%


% of

Total0.0% 0.0% 0.0% 24.4% 0.0% 24.4%

Total Count 3 9 13 12 4 41

% of

Total7.3% 22.0% 31.7% 29.3% 9.8% 100.0%

From the result of crosstab of facilities can be described as:

In cluster 1, we have 3 extremely agree with a percentage 7.3%, 0 agree with a percentage 0%,

and 0 not agree with a percentage 0.0%.

In cluster 2, we have 6 extremely agree with a percentage 14.6%, 3 agree with a percentage

7.3%, and 0 not agree with a percentage 0.0%.


22.0%, and 0 not agree with a percentage 0%.





4.4.4 Favor

4.5 Favor Crosstab

cluster

Total1.00 2.00 3.00 4.00 5.00

Favor Extremely

Agree

Count 3 8 13 4 1 29

% of

Total7.3% 19.5% 31.7% 9.8% 2.4% 70.7%


% of

Total0.0% 2.4% 0.0% 19.5% 7.3% 29.3%

Total Count 3 9 13 12 4 41

% of

Total7.3% 22.0% 31.7% 29.3% 9.8% 100.0%

From the result of crosstab of Favor can be described as in cluster 1, we have 3

extremely agree with a percentage 7.3%, 0 agree with a percentage 0%, and 3 not agree with a

percentage 7.3%.

In cluster 2, we have 8 extremely agree with a percentage 19.5%, and 1 agree with a

percentage 2.4%.


percentage 0%.


percentage 19.5%.


percentage 7.3%.

4.4.5 Comfortable Ambiance

4.6 Comfortable Ambiance Crosstab

cluster

Total1.00 2.00 3.00 4.00 5.00

Comfortable Ambiance

Extremely Agree

Count 3 7 13 10 0 33

% of Total 7.3% 17.1% 31.7% 24.4% 0.0% 80.5%


% of Total 0.0% 2.4% 0.0% 4.9% 7.3% 14.6%


% of Total 0.0% 2.4% 0.0% 0.0% 2.4% 4.9%

Total Count 3 9 13 12 4 41

% of Total 7.3% 22.0% 31.7% 29.3% 9.8% 100.0%

From the result of crosstab of Favor can be described as in cluster 1, we have 3

extremely agree with a percentage 7.3%, 0 agree with a percentage 0%, and 0 not agree with a

percentage 0%.




percentage 0%, and 0 not agree with a percentage 0%.


percentage 4.9%, and 0 not agree with a percentage 0%.

In cluster 5, we have 0 extremely agree with a percentage 0%, 3 agree with a percentage


4.5 Results Crosstab Variable Profiling

4.5.1 Gender * Profile

Table 4.7 Gender crosstab

Cluster

Total1.00 2.00 3.00 4.00 5.00

Gender Male Count 3 4 10 7 3 27

% of

Total7.3% 9.8% 24.4% 17.1% 7.3% 65.9%

Female Count 0 5 3 5 1 14

% of

Total0.0% 12.2% 7.3% 12.2% 2.4% 34.1%

Total Count 3 9 13 12 4 41

% of

Total7.3% 22.0% 31.7% 29.3% 9.8% 100.0%

From the result of crosstab of gender can be described as in cluster 1, we have 3 male

with a percentage 7.3% and 0 female with a percentage 0.0%.

In cluster 2, we have 4 male with percentage 9.8% and 5 female with a percentage

12.2%.

In cluster 3, we have 10 male with a percentage 24.4% and 3 female with a percentage

7.3%.

In cluster 4, we have 7 male with a percentage 17.1% and 5 female with a percentage

12.2%.

In cluster 5, we have 3 male with percentage 7.3% and 1 female with percentage 2.4%.

4.5.1 Age * Profile

Table 4.8 Age crosstab

cluster

Total1.00 2.00 3.00 4.00 5.00

Age <=25 Count 0 2 3 4 2 11

% of

Total0.0% 4.9% 7.3% 9.8% 4.9% 26.8%

26-35 Count 1 2 8 7 2 20

% of

Total2.4% 4.9% 19.5% 17.1% 4.9% 48.8%

>=36 Count 2 5 2 1 0 10

% of

Total4.9% 12.2% 4.9% 2.4% 0.0% 24.4%

Total Count 3 9 13 12 4 41

% of

Total7.3% 22.0% 31.7% 29.3% 9.8% 100.0%

In cluster 1, we have 0 people less than 25years old with a percentage 0.0%; 1 people

between 26-35 years old with a percentage 2.4 %; 2 people more than 36 years old with a

percentage 7.3%.

In cluster 2, we have 2 people less than 25 years old with a percentage 4.9 %; 2 people

between 26-35 years old with a percentage 4.9%; 5 people more than 36 years old with a

percentage 12.2%.

In cluster 3, we have 3 people less than 25 years old with a percentage 7.3%; 8 people


percentage 4.9%.



percentage 2.4%.



percentage 0%.

4.5.2 Job * Profile

Table 4.9 Job crosstab

cluster

Total1.00 2.00 3.00 4.00 5.00

Job Entrepreneurship Count 0 5 3 4 0 12

% of

Total0.0% 12.2% 7.3% 9.8% 0.0% 29.3%

Office

employees

Count 0 1 5 2 1 9

% of

Total0.0% 2.4% 12.2% 4.9% 2.4% 22.0%

Government

employees

Count 2 1 3 2 1 9

% of

Total4.9% 2.4% 7.3% 4.9% 2.4% 22.0%

Other jobs Count 1 2 2 4 2 11

% of

Total2.4% 4.9% 4.9% 9.8% 4.9% 26.8%

Total Count 3 9 13 12 4 41

% of

Total7.3% 22.0% 31.7% 29.3% 9.8% 100.0%

From the result of crosstab of jobs can be described as:

In cluster 1, we have 0 job entrepreneurship with percentage 0.0%; 0 job office employees

with a percentage 0.0%; 2 job Government employees with a percentage 4.9%; 1 job other

with a percentage 2.4%.













4.5.3 Intensities * Profile

Table 4.10 Intensities crosstab

Cluster

Total1.00 2.00 3.00 4.00 5.00

Intensities Seldom Count 1 2 6 1 2 12

% of

Total2.4% 4.9% 14.6% 2.4% 4.9% 29.3%

Several times Count 1 2 3 7 2 15

% of

Total2.4% 4.9% 7.3% 17.1% 4.9% 36.6%

Often Count 1 5 4 4 0 14

% of

Total2.4% 12.2% 9.8% 9.8% 0.0% 34.1%

Total Count 3 9 13 12 4 41

% of

Total7.3% 22.0% 31.7% 29.3% 9.8% 100.0%

From the result of crosstab of gender can be described as in cluster 1, we have 1

seldom intensity with a percentage 2.4%; 1 several times intensity with a percentage 2.4%; 1

often intensity with percentage 2.4%.

In cluster 2, we have 2 seldom intensity with a percentage 4.9%; 2 several times

intensity with a percentage 4.9%; 5 often intensity with percentage 12.2%.







4.6 Characteristics of Each Cluster

To divide the entire customers into some clusters,

a. Requirement in cluster 1 are: The first clusters are dominated with male (7.3%)

population in this cluster. The range of this the age is ≥ 36 years old with 4.9%

and majority is Government employees with 4.9%. The intensities in this

cluster have same range, there are: seldom, several times, and often all of them

2.4%.

b. Requirement in cluster 2 are: the second clusters female 12.2% dominated than

male, age ≥ 36 years old with 12.2%. And the profession are dominated

Entrepreneurship with 12.2%. The intensities of respondent in this cluster are

often with 12.2%.

c. Requirement in cluster 3 are: The third clusters are dominated with male with

percentage 24.4%, age between 26-35 years old (19.5%). And the profession is

dominated with Office employees (12.2%). The intensities of respondent in this

cluster are seldom with 14.6%.

d. Requirement in cluster 4 are: The fourth clusters are dominated with male

(17.1%) with age between 26-35 years old (17.1%). And the professions are

dominated same with 9.8% each of them are entrepreneurship and other jobs.

The intensities of respondent in this cluster are several times with 17.1%.

e. Requirement in cluster 5 are: The fifth clusters are dominated with male

(7.3%), ages are same between ≤ 25 years old and 26-35 years old with 4.9%.

And the profession is dominated with other jobs (4.9%). The intensities of

respondent in this cluster are same with 4.9% each of them are seldom and

several times.

We use squared Euclidean distance from dendrogram graphic using single linkage,

and then we got the result below:

Table 4.12 Recapitulation Cluster from Dendrogram

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5

1. Garindra

2. Dicky

3. Tito

1. Tugiyati

2. Marwah

3. Maya

4. Yuni

5. Annisa

6. Reza

7. Dalmindi

8. Hardi

9. Anggarista

1. Wawan

2. Sharno

3. Fantri

4. Tommy

5. Yudha

6. Singgih

7. Retno

8. Vicky

9. Rizky

10. Egi

11. Kusno

12.Sulistyowati

13. Cecil

1. Partiyah

2. Yanto

3. Ikhsanudin

4. Harwanto

5. Wijang

6. Dina

7. Danto

8. Desti

9. Sugeng

10. Jati

11. Amoy

12. Dila

1. Lina

2. Andreas

3. Budianto

4. Baerozi

4.7 Consumer response to the variable

1. Cluster 1: Garindra, Dicky, and Tito because they fulfill requirement in cluster 1 and the

requirement are: The first clusters are dominated with male (7.3%) population in this cluster.

The range of this the age is ≥36 years old with 4.9% and majority is Government employees

with 4.9%. The intensities in this cluster have same range, there are: seldom, several times,

and often all of them 2.4%.

2. Cluster 2: Tugiyati, Marwah, Maya, Yuni, Annisa, Reza, Dalmindi, Hardi, and Anggarista

because they fulfill requirement in cluster 2 and the requirement are: the second clusters

female 12.2% dominated than male, age ≥ 36 years old with 12.2%. And the profession are

dominated Entrepreneurship with 12.2%. The intensities of respondent in this cluster are often

with 12.2%.

3. Cluster 3: Wawan, Sharno, Fantri, Tommy, Yudha, Singgih, Retno, Vicky, Rizky, Egi,

Kusno, Sulistyowati, and Cecil because they fulfill requirement in cluster 3 and the

requirement are: The third clusters are dominated with male with percentage 24.4%, age

between 26-35 years old (19.5%). And the profession is dominated with Office employees

(12.2%). The intensities of respondent in this cluster are seldom with 14.6%.

4. Cluster 4: Partiyah, Yanto, Ikhsanudin, Harwanto, Wijang, Dina, Danto, Desti, Sugeng, Jati,

Amoy, and Dila because they fulfill requirement in cluster 4 and the requirement are: The

fourth clusters are dominated with male (17.1%) with age between 26-35 years old (17.1%).

And the professions are dominated same with 9.8% each of them are entrepreneurship and

other jobs. The intensities of respondent in this cluster are several times with 17.1%.

5. Cluster 5: Lina, Baerozi, Andreas, and Budianto because they fulfill requirement in cluster 5

and the requirement are: The fifth clusters are dominated with male (7.3%), ages are same

between ≤ 25 years old and 26-35 years old with 4.9%. And the profession is dominated with

other jobs (4.9%). The intensities of respondent in this cluster are same with 4.9% each of

them are seldom and several times.

4.8 Benefit Knowledge of Cluster formed for the research object (the object of study in

accordance with the study case location)

Cluster analysis is the organization of a collection of patterns into groups (clusters)

based on the similarity. The patterns in a cluster will have the same characteristics / properties

than the patterns in the other cluster. Clustering methodology is more suitable for the

exploration of the relationship among the data to make an assessment of the structure.

From the result of the choosen cluster we can take the advantage which is:

1. Understand the customer satisfaction.

2. Understand customer demand.

3. Identify the improvement of product in SGPC Bu Wiryo.

According to the journal of Dyah, Herdiana the reason why we choose the variable

cleanliness, service, facilities, favor, and comfortable ambiance is to know the reason whether

customers just wanted to try but did not want to go back again or not,

to determine the variables products and services (restaurants) that need to be improved so that

customers purchasing resilience and increased the number of recommendations, and to find

the right marketing strategy that can increase resilience to buy and the number of

recommendations undertaken to implement in the restaurant by improving the quality of the

product which offered in RM SGPC Bu Wiryo

2. The biggest score of clusters is cluster one which have 31.7% domination than other cluster

with the characteristic of male, 26-35years old, office employee, and have seldom intensity

going to the Restaurant SGPC Bu Wiryo.

CHAPTER V

CONCLUSION AND RECOMMENDATION

5.1. Conclusion

1. What indicators that significantly affect the observed variables in this study?

There are some variables that can use as the variables to know the customers satisfaction,

which are:

a) Cleanliness

It also affects consumers to be interested in a new restaurant and also influence

consumer behavior restaurants. This needs to get more attention because if cleanliness is

not maintained then consumers will be reluctant to visit again in the RM. SGPC Bu

Wiryo (Susanti, 2013)

b) Service

Because of the service can improve customer satisfaction. Quality of service is included

in the inseparability characteristics in services, therefore one way of improving the

quality of the waitress is through recruitment through better selection and training of

employees. (Susanti, 2013)

c) Facilities

It also affects consumers in layout of the room to be interested in a restaurant for adding

consumers satisfaction when all consumers eating their food (Susanti, 2013)

d) Favour.

It also affects consumers to be interested in a restaurant and also influence consumer

behavior restaurants (Susanti, 2013)

e) Comfortable ambiance

Ambience can be seen from how the interior design and exterior. Because the

restaurant RM. SGPC Bu Wiryo positioned as a family restaurant, the ambiance which

are presented in the matter is a family atmosphere (Susanti, 2013)

2. How much and how the characteristics of each cluster is formed?

From the dendrogram we can decide the cluster formed. There are :

a) 1stCluster : Garindra, Dicky, Tito

b) 2ndCluster : Tugiyati, Marwah, Maya, Yuni, Annisa, Reza, Dalmindi,

Hardi, Anggarista

c) 3rdCluster : Wawan, Sharno, Fantri, Tommy, Yudha, Singgih, Retno,

Vicky, Rizky, Egi, Kusno, Sulistyowati, Cecil

d) 4th Cluster : Partiyah, Yanto, Ikhsanudin, Harwanto, Wijang, Dina, Danto,

Desti, Sugeng, Jati, Amoy, Dila

e) 5th Cluster : Lina, Baerozi, Andreas, Budianto

The characteristic of each cluster is:

a. 1stCluster : The first clusters are dominated with male (7.3%) population in

this cluster. The range of this the age is ≥36 years old with 4.9% and majority is

Government employees with 4.9%. The intensities in this cluster have same range,

there are: seldom, several times, and often all of them 2.4%.

b. 2ndCluster : the second clusters female 12.2% dominated than male, age ≥

36 years old with 12.2%. And the profession are dominated Entrepreneurship with

12.2%. The intensities of respondent in this cluster are often with 12.2%.

c. 3rdCluster : The third clusters are dominated with male with percentage

24.4%, age between 26-35 years old (19.5%). And the profession is dominated with

Office employees (12.2%). The intensities of respondent in this cluster are seldom with

14.6%.

d. 4th Cluster : The fourth clusters are dominated with male (17.1%) with age

between 26-35 years old (17.1%). And the professions are dominated same with 9.8%

each of them are entrepreneurship and other jobs. The intensities of respondent in this

cluster are several times with 17.1%.

e. 5th Cluster : The fifth clusters are dominated with male (7.3%), ages are

same between ≤ 25 years old and 26-35 years old with 4.9%. And the profession is

dominated with other jobs (4.9%). The intensities of respondent in this cluster are same

with 4.9% each of them are seldom and several times.

5.2. Recommendation

1. For The Owner

By seeing the conclusion on this research, the owner can see the majority of their

customers and the jobs of them. It will help them to increase the customers’ satisfaction to

gain more customers, because it will impact their profit too.

2. For The Researcher

The researcher can implemented the clustering method in the real world and solve

when the problem happen.

3. For Further Research

There are many advantages using this method, clustering method. In the next research,

the researcher can use this method to identify the customers’ trust or use this method to

identify the others problem like segmenting the market and identifying the new product

opportunities.

REFERENCE

Susanti, Herdiana Dyah (2013) Perancangan servis untuk menentukan strategi bersaing di

rumah makan warung kuning Surabaya.

Selnes, F. (1993). “An Examination of the Effect of Product Performance on Brand

Reputation, Satisfaction and Loyalty” Journal of Marketing, Vol. 27 No. 9. Pp 19-35.

Pramudyo, Anung (2012), “Pengaruh Citra Merek. Terhadap Loyalty Melalui Kepuasan

Sebagai Intervening” JBMA – Vol. 1, No 1, Agustus 2012.

Berry, Steven (2006), “Product Quality and Market Size” January 12.

ATTACHMENT

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