Implementation of the Naïve Bayes Algorithm for Predicting the On-Time Graduation of Informatics Engineering Students

Abstract

Timely graduation is one of the important indicators used to measure the quality of academic performance in higher education institutions. However, the Informatics Engineering Study Program at the Faculty of Engineering, Universitas Islam Kuantan Singingi, still faces challenges related to students who are unable to complete their studies on time. This study aims to predict students timely graduation using the Naïve Bayes algorithm with the Knowledge Discovery in Database (KDD) approach. The research process consists of several stages, including data selection, data preprocessing, transformation, data mining, and evaluation. The data used in this study were obtained from students who completed their studies in 2025 and included several academic attributes such as gender, study duration, numerical grades, letter grades, and graduation status. The Naïve Bayes algorithm was applied to classify and predict whether students would graduate on time based on the probability of previous academic data. The results show that students with good academic performance tend to have a higher probability of graduating on time. Model evaluation using cross-validation produced the best performance in the 3-fold scenario, achieving an accuracy of 88.73%, precision of 64.62%, recall of 45.72%, and a kappa value of 0.451. These findings indicate that the Naïve Bayes algorithm is sufficiently effective for predicting students timely graduation.