Prediksi Penerimaan SNMPTN Menggunakan Algoritma C4.5 Dan Naive Bayes

Abstract

School is a formal education unit where teachers teach various disciplines which aim to make students become superior human beings. Education is important for humans. In general, people in Indonesia want their sons and daughters to be able to get an education at State Universities (PTN). There are many paths that can be taken to PTN, one of which is the National Selection for State University Entrance (SNMPTN) which is the PTN entrance route without a test or selection of report cards. However, the quota for acceptance of PTN through the SNMPTN pathway is at least 20% and the passing grade of each PTN cannot be determined so that the possibility of being accepted into PTN through the SNMPTN route is unknown. Therefore, to be able to predict students who can be accepted into PTN through the SNMPTN path, predictions of SNMPTN acceptance are made using classification data mining with the C4.5 and Naive Bayes algorithm. The goal is that with students knowing the possibility of being accepted into PTN through the SNMPTN pathway, it is hoped that more students can be accepted by PTN through the SNMPTN pathway. This study shows that the prediction of PTN acceptance through the SNMPTN path using data mining classification with the C4.5 algorithm produces a much higher accuracy of 85,09% and the AUC value 0,873, while the Naive Bayes algorithm produces an accuracy of 63,01% and the AUC value.