Comparative Analysis of Classification Algorithms Using Feature Selection Techniques to Predict On-Time Student Graduation

On-time graduation rates are crucial for universities, impacting institutional performance and student success.At Sebelas Maret University, only 32% of the 2019-2020 postgraduate cohort graduated on time, exemplifying a common higher education challenge.This study compares naï ve bayes, (NB) K-nearest neighbor (KNN), and decision tree (DT) algorithms, chosen for their effectiveness in educational data mining (EDM).Forward selection (FwS) and backward elimination (BE) techniques were implemented to optimize feature selection (FS), balancing model complexity and predictive power.Previous studies have primarily focused on graduation prediction, but few have thoroughly compared FS methods.This study compares NB, KNN, and DT algorithms, implementing FwS and BE for feature optimization.Results show that FS improved model performance across all algorithms.KNN and DT algorithms showed a more favorable impact with FwS, while BE proved more effective for the NB algorithm.The KNN algorithm with FwS achieved the highest accuracy at 83.8%, a significant improvement from its baseline accuracy of 76.64%.These findings can guide the development of support systems to improve on-time graduation rates, potentially benefiting institutions facing similar challenges.By evaluating these features, institutions can enhance their educational quality and support students in achieving timely graduation.