ANALISIS KINERJA ALGORITMA C4.5 DAN NAÏVE BAYES UNTUK MEMPREDIKSI PRESTASI SISWA SEKOLAH MENENGAH KEJURUAN - Astrid Noviriandini; Nurajijah Nurajijah (LPPM Nusa Mandiri, 2019)
This research informs students and teachers to anticipate early in following the learning period in order to get maximum learning outcomes. The method used is C4.5 decision tree algorithm and Naïve Bayes algorithm. The purpose of this study was to compare and evaluate the decision tree model C4.5 as the selected algorithm and Naïve Bayes to find out algorithms that have higher accuracy in predicting student achievement. Learning achievement can be measured by the value of report cards. After comparison of the two algorithms, the results of the learning achievement prediction are obtained. The results showed that the Naïve Bayes algorithm had an accuracy value of 95.67% and the AUC value of 0.999 was included in Excellent Clasification, for the C4.5 algorithm the accuracy value was 90.91% and the AUC value of 0.639 was included in the state of Poor Clasification. Thus the Naïve Bayes algorithm can better predict student achievement.