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15 February 2012

Predicting Students’ Academic Success Using Artificial Neural Network

PREDICTING STUDENTS’ ACADEMIC SUCCESS USING ARTIFICIAL NEURAL NETWORK

A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF TELKOM INSTITUTE OF TECHNOLOGY

BY ALDI RAMDHANI HERAWAN 213100001

Prof. The Houw Liong, Ph.D Supervisor

Shaufiah, M.T. Co-Supervisor

ABSTRACT

Predicting Students’ Academic Success Using Artificial Neural Network

Aldi Ramdhani Herawan

Supervisor : Prof. The Houw Liong, Ph.D

Co-Supervisor : Syaufiah, M.T

In an effort to improve the quality and competitiveness of scholars, universities must have specific strategies to achieve its objectives. Implementation of these strategies would require preparation and adjustments to the problem at hand. For that matter, need early identification what factors affect the success of a student's study. The success of a student's study can be viewed with a grade point average (GPA) student. This study seeks to identify what factors are affecting the success of a student's study indicated by the GPA. These factors are then used as input in the GPA prediction model using Artificial Neural Network (ANN). This study was also conducted on the reduction of data dimension using Principal Component Analysis (PCA). Finally, this study compares the results of predicted GPA, with the input data that has not been reduced and data have been reduced. For variables JUMLAH MATA KULIAH (number of courses) , JUMLAH SKS (sum of credits taken), JUMLAH SKS LULUS (sum of credits passed), and JUMLAH MUTU (average grades in the first year), component coefficients which measure correlations with GPA(average grades in the second year) are extracted. Component coefficient values for each variable are greater than 0.50, and the highest value is 0.958 for JUMLAH SKS LULUS. This shows that component extracted is evenly affected by these variables.

Keywords: Students’ Success, GPA, Prediction, Artificial Neural Network (ANN), Principal Component Analysis (PCA)

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