Prediction Model Graduation Student with Naive Bayes Algorithm - Lolyta Damora Simbolon; Rani Farida Sinaga; Lena Rosdiana Pangaribuan; Sofy Kristiani Manalu (Lembaga Pendidikan, Penelitian, dan Pengabdian Kamandanu, 2026)
One of indicator important in evaluation quality something college tall is level graduation students. Graduation rate appropriate time students become a reflection from the quality of the learning process which ultimately also influences accreditation institutions or study program. The purpose of this research is to analyze how to increase student graduation rates and reduce dropout rates through more accurate and effective data-based policy recommendations. There are Lots factor affecting graduation student so that need determined factors significant influence level graduation said. With do analysis to data and predictions graduation students, institutions expected can give appropriate intervention for increase level graduates and identify at-risk students experience late graduation. In research this using the Naïve Bayes method for predict graduation student based on various factors. The data used in study this is the data obtained from students of the Mathematics Education Study Program, Faculty of Teacher Training and Education, HKBP Nommensen University. The results of study this show accuracy 80%, precision 88.24%, and recall 88.24%.