APPLICATION OF LENGTH OF STUDY AND DEGREE OF EXCELLENCE PREDICTION FOR INFORMATICS DEPARTMENT STUDENTS OF UMS
USING NAÏVE BAYES METHOD
FINAL PROJECT
Submitted as a Partial Fulfillment of the Requirements for
Getting the Bachelor Degree in Department of Informatics
Faculty of Communications and Informatics
Universitas Muhammadiyah Surakarta
By:
MUH AMIN NURROHMAT
L200112010
DEPARTMENT OF INFORMATICS
FACULTY OF COMMUNICATIONS AND INFORMATICS
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DEDICATION
Thank you to Allah SWT who has given me his blessing, so the writer
complete this research. This research is dedicated to
1. My beloved parent, Suwarlan and Siti Faizah that always support and pray for
my best. Thank you for always be there for me.
2. My Brother, Muh Bayu Kurniawan and all my family that always give
support and advice to me.
3. My beloved Hasna Fathina who always given love, passion and motivation.
4. My class mate for International Informatics 2011 (Nega, Kemal, Charis,
Sunu, Chintya, Aulia, Elly, Nidha, Cori).
5. HIMATIF and BEM FKI which has given opportunity to improve soft skill,
and knowledge.
6. Families of Informatics UMS which has given many knowledge and
experiences.
ACKNOWLEDGEMENT
Alhamdulillahirabbil’alamin, all praise and gratitude to Allah SWT, who has given
us the blessing and guidance so the writer can finish the final project by the titled
"Application of Length of Study And Degree of Excellence Prediction For
Informatics Department Students of UMS UsingNaïve BayesMethod".
This final project is structured as obligations to complete the bachelor degree
of Department of Informatics, Faculty of Communication and Informatics,
Muhammadiyah University of Surakarta. The writer realizes that this final project is
far from perfection, therefore suggestions from reader is welcome by the writer.
The realization of this research cannot be separated from the support of
various parties. Therefore, the writer would like to say thanks and appreciations to:
1. Allah SWT who has given us the mercy and blessing until writer can
complete this research
2. Husni Thamrin, S.T, M.T., Ph.D as the Dean of the Faculty of
Communications and Informatics, University of Muhammadiyah Surakarta.
3. Dr. Heru Supriyono, M.Sc as the Head of Department of Informatics,
University of Muhammadiyah Surakarta.
4. Endah Sudarmilah, S.T, M.Eng as academic advisor who has given a
knowledge and direction.
5. Yusuf Sulistyo Nugroho, S.T, M.Eng as research advisor who always give
ABSTRACT
Informatics department of Muhammadiyah University of Surakarta has large data. The data are active students and graduate students. Every year, the data becomes larger. On the other hand, the department cannot manage the data well, thus it means that if the data is larger, then the information is smaller. The solution to solve the problem is that the data must be converted into information. This research discusses how to maximize the data into information using data mining technique. This research uses Naïve Bayes method. It is used to analyze the data, especially in the process of pattern recognition, predicting length of study, and predicting the degree of excellence. After processing the data, the application will display the report, the summary report, and suggestion. Based on the results, the application helps Informatics department to find a solution and take a decision to determine the policy. It is in order to decide where Informatics department will promote its department. Moreover, if Informatics department can recruit good student, it can improve the quality of Informatics department.
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TABLE OF CONTENTS
TITLE... i
APPROVAL... ii
ACCEPTANCE ... iii
CONTRIBUTION LIST ... iv
DEDICATION ... v
ACKNOWLEDGEMENT ... vi
ABSTRACT... viii
TABLE OF CONTENT ... ix
LIST OF TABLE ... xii
LIST OF FIGURE... xiii
CHAPTER 1 INTRODUCTION... 1
1.1. Background of Study ... 1
1.2. Problem Statement ... 3
1.3. Limitation of Study ... 3
1.4. Objective ... 4
1.5. Benefit... 4
1.6. Report Organization... 5
CHAPTER 2 LITERATURE REVIEW... 6
2.1. Research Study... 6
2.2. Basic Theory ... 9
2.2.2. The Definition of Length of Study and Degree of
Excellence ... 9
2.2.3. Data Mining... 10
2.2.4. Naïve Bayes... 11
2.2.5. Java ... 11
CHAPTER 3 RESEARCH METHOD... 13
3.1. Place and Period of Research... 13
3.1.1. Period of Research ... 13
3.1.2. Place of Research ... 13
3.2. Main and Supporting Tools ... 13
3.2.1. Hardware... 13
3.2.2. Software ... 14
3.3. Research Method ... 14
3.3.1. Data Mining Analysis ... 14
3.3.1.1. Collecting The Data ... 15
3.3.1.2. Data Needs ... 16
3.3.1.3. Cleaning Data... 17
3.3.1.4. Data Transformation ... 18
3.3.1.5. The Use ofNaïve BayesMethod ... 19
3.3.2. Flowchart ... 21
3.3.3. Use Case Diagram... 22
3.3.3.1. Use Case Diagram identification ... 22
3.3.3.2. Use Case Diagram... 22
CHAPTER 4 RESULT AND ANALYSIS... 23
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4.1.1.1. Login Form ... 23
4.1.1.2. Main Form ... 24
4.1.1.3. Upload Data Training Form... 25
4.1.1.4. Upload Data Testing Form... 25
4.1.1.5. Mining Result Form ... 26
4.1.1.6. About Form ... 30
4.1.1.7. “Petunjuk”Form ... 30
4.1.2. Process Manual Calculation Naïve Bayes Method ... 31
4.2. System Testing... 36
CHAPTER 5 CLOSING... 39
5.1. Conclusion ... 39
5.2. Suggestion... 39
BIBLIOGRAPHY... 41
PROFIL... 43
LIST OF TABLE
3.1 Graduation Data ... 15
3.2 Student Data ... 16
3.3 Attribute List ... 17
3.4 Degree of Excellence based on GPA for D4, S1... 18
3.5 Data Transformation ... 19
3.6 Use Case Diagram Identification ... 22
4.1. Training Data... 31
4.2. Testing Data ... 31
4.3. Login Function ... 37
4.4. Upload Data (Training) Function ... 37
4.5. Upload Data (Testing) Function... 37
4.6. Process of Mining Function ... 38
xiii
LIST OF FIGURE
3.1. Venn Diagram ... 20
3.2. Flowchart of Application ... 21
3.3. User Use Case ... 22
4.1. Login Form... 24
4.2. Main Form... 24
4.3. Upload Data Training Form ... 25
4.4. Upload Data Testing Form ... 26
4.5. “Hasil Mining” Table ... 27
4.6. “Rangkuman”Report ... 27
4.7. “Rangkuman” Graphic Report... 28
4.8. “Saran” Report... 29
4.9. “Saran” Graphic Report... 29
4.10. About Form ... 30
