Implementasi Algoritma Naïve Bayes Menggunakan Feature Forward Selection dan SMOTE Untuk Memprediksi Ketepatan Masa Studi Mahasiswa Sarjana - Dede Kurniadi; Fitri Nuraeni; Sri Mulyani Lestari (Jurnal Sistem Cerdas, 2022)
The punctuality of students in completing their studies is an important aspect of the study program. Because there are still students who have not been able to complete their studies on time. The purpose of this study is to determine the factors that influence students in completing their studies by extracting student academic data to obtain a classification model that can be used to predict the accuracy of the study period. The classification method for predicting the accuracy of the student's study period uses the Naive Bayes algorithm using the Feature Forward Selection and SMOTE. The method for data processing in this study uses CRISP-DM. The results of this study are in the form of a classification model to predict the accuracy of the study period of students who obtain a fairly high accuracy value of 87.13%, a recall value of 83.82%, and a precision value of 89.76%, and an AUC value of 0.92. included in the category of Excellent Classification. The use of SMOTE has succeeded in handling Imbalanced Class on the data, and the application of Feature Forward Selection resulted in 5 factors that most influence the accuracy of the student's study period, namely the attributes of Gender, School Category, Year of Entry, Study Program and Grade Point Average for the third semester. The prediction model generated using the Naïve Bayes algorithm, Feature Forward Selection, and SMOTE is expected to help study programs to find out earlier the possibility of students completing their studies on time or not on time.