Implementation of Machine Learning Algorithms for Predicting Student Academic Performance
DOI:
https://doi.org/10.57152/ijatis.v3i1.1871Keywords:
Academic Performance, K-Nearest Neighbors, Naïve Bayes, Random Forest, Support Vector MachineAbstract
This study examines the effectiveness of five data mining algorithms, K-Nearest Neighbor (K-NN), Naive Bayes, Decision Tree, Random Forest, and Support Vector Machine (SVM), in predicting and comparing students' academic performance. The goal is to evaluate the following: the study data includes average grades, learning motivation, study hours per week, and parental support. The data underwent preprocessing steps, including normalization, outlier removal, and splitting into training and test sets. Model performance was evaluated using accuracy, precision, and recall metrics. The results indicate that the Random Forest algorithm performed the best, followed by the Decision Tree, which also demonstrated strong performance. The SVM and Naive Bayes algorithms provided excellent results, while K-NN performed poorly due to class overlap in the data. The conclusion of this study is that the Random Forest algorithm is the most effective method for predicting students' academic performance and significantly contributes to data-driven analysis to improve the quality of education.
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