A Comparison of Machine Learning Algorithms in Predicting Students' Academic Performance

Baye, Juanda Alra
Alfaridzi, Gemma Tahmid
Abdurrahim, Hilmy
Adinda, Abid Aziz
Athallah, Muhammad Rakha
Ramadhan, Muhammad Zahid

PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Vol. 3 No. 2: PREDATECS January 2026

Predicting students’ academic performance enables early interventions and data-driven planning in education. We compare five machine-learning algorithms Decision Tree, K-Nearest Neighbor, Naive Bayes, Random Forest, and Support Vector Machine on a publicly available dataset of 1,001 students, evaluated with Accuracy, Precision, Recall, and F1-Score. The Decision Tree achieved the highest performance, with perfect scores on this dataset, while SVM (?82% F1) and Random Forest (?81% F1) were competitive. These results suggest that simple, interpretable models can be highly effective when features are clean and predictive; however, the Decision Tree’s perfection also indicates potential overfitting and warrants further validation on larger, more diverse samples. The study underscores how model choice should reflect dataset characteristics and practical deployment goals in educational settings, informing early-warning systems and targeted support programs.