Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Naïve Bayes Dan Decision Tree Pada Universitas Stella Maris Sumba - Julianti Suwartini Inda Sari; Elfira Umar; Lidia Lali Momo (CV. ITTC INDONESIA, 2024)
Timely graduation itself is one of the indicators of the success of students' academic performance. The study period regulations are already set in the provisions of the Minister of Education and Culture of Indonesia. To address this issue, there needs to be a technique to predict graduation. One of the techniques commonly used is data mining. In this study, the authors will compare two data mining methods, namely Naive Bayes Classifier and Decision Tree, to obtain the method with the best accuracy in predicting student graduation. The attributes used for Data Mining Classification consist of 10 attributes: Student ID, Gender, Student Status, Age, Semester 1 Grade Point Average, Semester 2 Grade Point Average, Semester 3 Grade Point Average, Semester 4 Grade Point Average, Cumulative Grade Point Average, and Result attribute. From the test results using RapidMiner tools with two methods that have been conducted, the Decision Tree (C4.5) obtained the accuracy result of 70.18%, and the Naïve Bayes method obtained the highest accuracy result of 71.24%.