Open AccessComputer-based Techniques for Predicting the Failure of Student Studies Using the Decision Tree method
Author(s) -
Dudy Mohammad Arifin,
Asep Id Hadiana
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/662/2/022112
Subject(s) - decision tree , graduation (instrument) , selection (genetic algorithm) , computer science , drop out , decision tree learning , machine learning , affect (linguistics) , tree (set theory) , artificial intelligence , data mining , mathematics , psychology , mathematical analysis , geometry , communication , economics , demographic economics
The purpose of this study is to predict students who have the potential to drop out of college so that the selection process for prospective students is more effective. Based on the problems that have been raised. The research method used was the forecasting method proposed to predict prospective students who drop out before entering college using the Decision Tree C4.5 method and Forward Selection. The tool used in this study used rapidminer 9.2. Based on the research results obtained, using 90% training data and 10% testing data resulted in an accuracy of 82.52% and obtained attribute models that affect the classification of student graduation, namely the Study Program and Age attributes.