Implementasi Data Mining Menggunakan Algoritma Naïve Bayes Untuk Prediksi Kelulusan Siswa - Punkastyo, Dimas Abisono; Septian, Fajar; Syaripudin, Ari (Journal of System and Computer Engineering, 2024)
The student graduation process involves a series of stages and requirements that must be met by each student. Graduation is an important step in education because it determines the student's ability to continue to the next level. In this research, student graduation data is processed using data mining techniques, especially the Naïve Bayes method, to predict student graduation. Attributes such as Practice Scores, School Exams (US), National Exams (UN), and Student Behavior are used in this prediction process. The research results show that from a population of 60 students, the model successfully predicted that 45 students would pass, and 15 students would not pass. The Naïve Bayes method was implemented using the orange application and produced an accuracy level of 98.33% with a precision level of 100.00%. These results show that the Naïve Bayes method is very effective in predicting student graduation with a high level of accuracy. Therefore, this data mining technique can be a valuable tool for educational institutions in improving the quality of student graduation outcomes.