the application of data mining in human resources

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THE APPLICATION OF DATA MINING IN HUMAN RESOURCES MANAGEMENT TO BUILD DATA MINING MODEL FOR EMPLOYEE PERFORMANCE IN OPERATOR AND JUNIOR MANAGEMENT LEVEL

(CASE STUDY: J COMPANY)

By Ira Mellisa 2-1551-019

MASTER’S DEGREE in

INFORMATION TECHNOLOGY

FACULTY OF ENGINEERING & INFORMATION

SWISS GERMAN UNIVERSITY The Prominence Tower

Jalan Jalur Sutera Barat No. 15, Alam Sutera Tangerang, Banten 15143 - Indonesia

January 2018

Revision After Thesis Defense on 15 January 2018

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Ira Mellisa

STATEMENT BY THE AUTHOR

I hereby declare that this submission is my own work and to the best of my knowledge, it contains no material previously published or written by another person, nor material which to a substantial extent has been accepted for the award of any other degree or diploma at any educational institution, except where the due acknowledgement is made in the thesis.

Ira Mellisa

_____________________________________________

Student Date

Approved by:

Dr. Eka Budiarto, S.T, M.Sc

_____________________________________________

Thesis Advisor Date

Dr. Amalia Zahra, S.Kom

_____________________________________________

Thesis Co-Advisor Date

Dr. Irvan S. Kartawiria, S.T, M.Sc

_____________________________________________

Dean Date

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Ira Mellisa ABSTRACT

THE APPLICATION OF DATA MINING IN HUMAN RESOURCES MANAGEMENT TO BUILD DATA MINING MODEL FOR EMPLOYEE PERFORMANCE IN OPERATOR AND JUNIOR MANAGEMENT LEVEL

(CASE STUDY: J COMPANY)

By Ira Mellisa

Dr. Eka Budiarto, S.T, M.Sc, Advisor Dr. Amalia Zahra, S.Kom, Co-Advisor

SWISS GERMAN UNIVERSITY

Human resources management was designed by the company to optimize employee performance in order to achieve company goals. The company goals were determined by the top management and accompanied by some strategies that need to be accomplished. The employee performance was assessed by the company based on the ability of employees in achieving company goals. Hence, employee qualification theory needs to be adopted by the company so that the company could obtain an overview of employee performance.

In the next stage, the company also need an effective method to predict performance, not only for employees but also for new applicants. The goals of this research are to get data mining models of the employee performance. By learning existing employee data, the performance of the new applicants could be predicted. The data mining model generated was derived from the application of data mining techniques on research materials. The study would produce the characteristic of new applicants who will give better performance than other applicants.

The study used data from a company in Indonesia (J Company). The data mining techniques will be applied in the data of operators (such as admins, clerks, cashiers, machine operators, and security officers) and junior management (junior staffs,

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Ira Mellisa supervisors, and junior executives). The data mining technique used is decision tree.

The decision tree technique was commonly used for a supervised learning data, while the support vector machine is a recent technique in data mining. The decision tree technique also has advantages compared others, because of its ability to produce information that is easy to understand. On the other hand, the support vector machine has a more accurate algorithm for predicting employee performance.

Keywords: data mining, employee performance prediction, decision tree, support vector machine

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Ira Mellisa

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Ira Mellisa DEDICATION

I dedicate this works to the future of the country I loved: Indonesia

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Ira Mellisa ACKNOWLEDGEMENTS

I wish to thank all the lecturer and all my friend in Swiss German University for their support, patience and good advice.

Mr. Dr. Eka Budiarto, S.T, M.Sc, my thesis advisor who has provided guidance and advice during research activities and thesis writing.

Mrs. Dr. Amalia Zahra, S.Kom, my thesis co advisors who gave me a good input regarding the research methodology.

Mr. Dr. Ir. Moh. A. Amin Soetomo, M.Sc, as Chairman of Business Informatics and IT Governance Program of Swiss German University who has given direction and motivation.

I would like to thank my family for their infinity support. Without their support, I would not be able to pass my hardest moments.

I realize that the preparation of this thesis is still far from perfect. Therefore, I am glad to accept any suggestions and criticisms that can help me to improve the quality of this thesis. Finally, I hope that this thesis can provide benefits for all those who need it.

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Ira Mellisa TABLE OF CONTENTS

Page

DEDICATION ... 6

ACKNOWLEDGEMENTS ... 7

LIST OF FIGURES ... 10

LIST OF TABLES ... 11

CHAPTER 1 – INTRODUCTION ... 12

1.1. Background ... 12

1.2. Objectives ... 13

1.3. Research Questions ... 14

1.4. Hypothesis ... 14

CHAPTER 2 - LITERATURE REVIEW ... 15

2.1. Theoretical Perspectives ... 15

2.2. Previous Studies ... 17

CHAPTER 3 – RESEARCH METHODS ... 20

3.1. Materials and Equipment ... 20

3.2. Analytical Method ... 22

CHAPTER 4 – RESULTS AND DISCUSSIONS ... 27

4.1. Data Preparation ... 27

4.2. Data Sampling for Operator Dataset ... 31

4.3. Analyze Accuracy of Decision Tree and Support Vector Machine Algorithm on Operator Dataset ... 33

4.3.1. Experiment 1: Operator dataset by using 10 folds cross validation ... 33

4.3.2. Experiment 2: Operator dataset with under-sampling 90% ... 35

4.3.6. Summary of hit rates and accuracy of decision tree and support vector machine for operator dataset... 43

4.4. The Most Affecting Attribute of Operator Dataset for Employee Performance ... 44

4.5. Data Sampling for Junior Management Dataset ... 45

4.6. Analyze Accuracy of Decision Tree and Support Vector Machine Algorithm on Junior Management Dataset ... 46

4.6.1. Experiment 1: Junior Management dataset by using 10 folds cross validation . 47 4.6.2. Experiment 2: Junior Management dataset with under-sampling 90% ... 49

4.6.3. Experiment 3: Junior Management dataset with under-sampling 80% ... 52

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4.6.4. Experiment 4: Junior Management dataset with under-sampling 70% ... 54

4.6.5. Summary of hit rates and accuracy of decision tree and support vector machine for junior management dataset ... 57

4.7. The Most Affecting Attribute of Junior Management Dataset for Employee Performance ... 58

CHAPTER 5 – CONCLUSIONS AND RECCOMENDATIONS ... 60

5.1. Conclusions ... 60

5.2. Recommendations ... 61

GLOSSARY ... 62

REFERENCES ... 63

CURRICULUM VITAE ... 65

APPENDIX ... 67