Penerapan Data Mining Untuk Prediksi Kelulusan Siswa Menggunakan Algoritma NaÃ¯ve Bayes Pada SMK Garuda - Endah Yaodah Kodratillah; Daririn Daririn; Candra Naya (Teknik Informatika, Universitas Pelita Bangsa, 2021)
Student graduation is a series of process stages that must be assessed by each student, including having to complete a predetermined number of lessons, and must meet the terms and conditions set by the school, because graduation is a reference so that they can continue their education to a higher level. and work in the business world and the industrial world. Therefore, this study will utilize data about student graduation by processing it using Data Mining to obtain information in the form of predictions of student graduation. The algorithm that will be used is the Naive Bayes algorithm. The attributes used in predicting student graduation are Practice Score, US (School Examination) and Student Behavior. Data Mining testing using the Naive Bayes algorithm produces an accuracy of 76.25%, precision 93.33%, recall 78.87% and an AUC value of 0.751. It can be concluded that the Naive Bayes algorithm has very good accuracy in this study. Keywords: Data mining, Classification, C4.5 Algoritma, Naive Bayes