PERANCANGAN APLIKASI PREDIKSI LAMA STUDI MAHASISWA BERDASARKAN MODEL KLASIFIKASI DATA LAMA STUDI MAHASISWA STMIK INDONESIA YANG TERBENTUK MENGGUNAKAN DECISION TREE

Redaksi Tim Jurnal

Petir
Vol 10 No 2 (2017): PETIR (Jurnal Pengkajian Dan Penerapan Teknik Informatika)

One way to improve quality in universities is through accreditation. One of the accreditation criteria is the student. Student’s performance must be monitored and evaluated. Regarding the study duration, the undergraduate bachelor’s degree programs typically takes four years to complete. It is important for the university staff to quickly identify which students are less likely to finish the degree on time. Therefore, it is necessary to predict the length of study for each student. The goal of this research is to predict study duration by building Decision Tree-based classifier model using NBTree algorithm. Then, an application is built by applying the classification model. Data used in this research are the grades and academic leave. Result shows that the Naïve Bayes Decision Tree classification model could predict study duration with the accuracy of 73.45%.