Penggunaan Datamining Untuk Memprediksi Masa Studi Mahasiswa di Universitas Muhammadiyah Sidoarjo Dengan Algoritma Naive Bayes - Muhammad Mursidil Arif; Hamzah Setiawan; Arif Senja Fitrani (Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen), 2023)
In the higher education, improving student performance and improving the quality of education is very important. The education system requires innovative ways to improve the quality of education, achieve the best results and minimize student failure rates. One of the innovative ways is to apply data mining to predict students' study period. The results of these predictions will help students or academic adviser to provide early warning or give more precise directions to each student, so that they can do the best things to increase the chances of graduating on time. In this study, 9 academic and non-academic variables were used, consisting of semester grade point index, Semesters 1, 2, 3 and 4, GPA, school origin (public/private), finance (constrained by financial problems or not), scholarship (whether get a scholarship or not), Student Affairs (active or not in the student program). The use of academic and non-academic data variables in this study aims to broaden the predictions of student graduation which are not only assessed from an academic point of view, but also look at non-academic factors. The data used is student’s data for the 2017-2018 Informatics study program at the Muhammadiyah University of Sidoarjo. This data is obtained from the Directorate of Information Systems Technology (DSTI) Muhammadiyah University of Sidoarjo as many as 200 data. Modelling using the naïve Bayes algorithm using Anaconda Navigator software with IDLE Jupyter Notebook and the Python programming language, after evaluation using the confusion matrix and accuracy score, the results obtained were 68% accuracy, precision value 0.67, recall 0.77 and f1-score 0.72. while the accuracy score evaluation value gets 67.35%