CLASSIFICATION MODELS FOR ACADEMIC PERFORMANCE: A COMPARATIVE STUDY OF NAÏVE BAYES AND RANDOM FOREST ALGORITHMS IN ANALYZING UNIVERSITY OF LAMPUNG STUDENT GRADES

Dian Kurniasari; Rekti Nurul Hidayah; Notiragayu Notiragayu; Warsono Warsono; Rizki Khoirun Nisa

doi:10.52436/1.jutif.2024.5.5.2066

Dian Kurniasari Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, Indonesia
Rekti Nurul Hidayah Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, Indonesia
Notiragayu Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, Indonesia
Warsono Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, Indonesia
Rizki Khoirun Nisa Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.5.2066

Keywords: Classification, Final grade, Naive bayes, Random forest, Student

Abstract

At the university, students are provided with a comprehensive assessment of their academic achievements for each course completed at the end of every semester. This study aimed to compare the effectiveness of two classification methods, the Naïve Bayes and the Random Forest methods, in classifying student learning outcomes. The research process is segmented into various stages: data selection, data preparation, model building and testing, and model evaluation. The findings indicated that the Naïve Bayes and Random Forest approaches exhibited superior accuracy levels when employing data splitting strategies, in contrast to k-fold cross-validation. Based on the examination, the Random Forest approach demonstrated superiority in identifying the scores of University of Lampung students, achieving an accuracy percentage of 99.38%. Notably, both techniques showed a substantial performance improvement using Gradient Boosting. The Naïve Bayes method attained an accuracy rate of 99.89%, while the Random Forest method reached 99.45%. The results demonstrate that employing the Random Forest classification method consistently leads to superior performance in identifying and classifying student grades. Furthermore, using Gradient Boosting in the boosting process has demonstrated its efficacy in enhancing the classification methods' accuracy. These findings significantly contribute to the comprehension and advancement of evaluation systems for assessing student learning outcomes in the university environment.

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