Fuzzy Decision Tree to Predict Student Success in Their Studies

Heri Bambang Santoso

doi:10.46336/ijqrm.v1i3.59

Abstract

The number of students graduating on time is one of the important aspects in the assessment of accreditation of a university. But the problem is still a lot of students who exceed the target time of graduation. Therefore, the prediction of graduation on time can serve as an early warning for the university management to prepare strategies related to the prevention of cases of drop out. The purpose of this research is to build a model using fuzzy decision tree to form the classification rules are used to predict the success of a student's study using fuzzy inference system. Results of this study was generated model of the number of classification rules are 28 rules when the value θr is 98% and θn is 3%, with the level of accuracy is 95.85%. Accuracy of Fuzzy ID3 algorithm is higher than ID3 algorithms in predicting the timely graduation of students.

Highlights

The quality of a university than can be seen by the average length of its graduates get a job, can be seen by the average length of study students
Negative correlation has a meaning that the higher the value of an attribute predictor of the long study period covered will be the smaller or faster
These attributes are used in this study

Summary

Introduction

The quality of a university than can be seen by the average length of its graduates get a job, can be seen by the average length of study students. The study program is obliged to monitor the progress of students study. Prediction graduate on time can serve as an early warning to the performance of students study. To make a prediction can be done in various ways, one of which can be done by using a data mining techniques. Kwik Kian Gie School of Business has a dataset at the Academic Information System that has not been fully utilized. It is unfortunate if the dataset is so large is not used for mining the information contained therein

Objectives

Methods

Results