Implementasi Algoritma Naïve Bayes Menggunakan Feature Forward Selection dan SMOTE Untuk Memprediksi Ketepatan Masa Studi Mahasiswa Sarjana

The punctuality of students in completing their studies is an important aspect the study program. Because there are still who have not been able to complete on time. purpose this determine factors that influence by extracting student academic data obtain a classification model can be used predict accuracy period. method for predicting student's period uses Naive Bayes algorithm using Feature Forward Selection and SMOTE. processing CRISP-DM. results form fairly high value 87.13%, recall 83.82%, precision 89.76%, AUC 0.92. included category Excellent Classification. use SMOTE has succeeded handling Imbalanced Class data, application resulted 5 most period, namely attributes Gender, School Category, Year Entry, Study Program Grade Point Average third semester. prediction generated Naïve algorithm, Selection, expected help programs find out earlier possibility time or