Abstract
Students who are not-active will affect the number of students who graduate on time. Prevention of not-active students can be done by predicting student performance. The study was conducted by comparing the KNN, SVM, and Decision Tree algorithms to get the best predictive model. The model making process was carried out by steps; data collecting, pre-processing, model building, comparison of models, and evaluation. The results show that the SVM algorithm has the best accuracy in predicting with a precision value of 95%. The Decision Tree algorithm has a prediction accuracy of 93% and the KNN algorithm has a prediction accuracy value of 92%.
Highlights
IntroductionThe accreditation will be better if more students graduate on time
Improving the quality of education and accreditation of departments is always endeavored by every college department
Prevention of not-active students can be done by predicting student performance
Summary
The accreditation will be better if more students graduate on time. Other research by applying Decision Tree algorithms such as; predictions of drop-out students from college based on GPA [5], analysis to predictive the accuracy of 4-year studies of student [6]. In addition to the Data Mining algorithm, using the Fuzzy method is done to predict student performance. Comparative algorithm research for predicting student performance had been carried out. From some studies about student performance by comparing several algorithms, no one had compared the KNN, SVM, and Decision Tree algorithms in predicting student performance. The research that had been done aims to compare algorithms (KNN, SVM, and Decision Tree) to get the best model for predicting student performance
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