Naïve Bayes Method Implementation in Selecting Olympiad Participants for Grade XI Students of SMA Kristen Waibakul

Abstract

The selection of academic Olympiad participants at SMA Kristen Waibakul has traditionally relied on manual judgement of report-card scores and teacher recommendations, a practice that is prone to subjectivity and inconsistency. This study develops and empirically evaluates a decision support approach that combines the Naïve Bayes classifier with a Greedy Allocation strategy to select eleventh-grade students for a school Olympiad team spanning nine academic fields: Mathematics, Physics, Economics, Chemistry, Biology, Geography, Informatics, Earth Science, and Astronomy. A training set of 80 students and an independent test set of 40 students, each described by seven subject scores, were discretised into High/Low categories using an 80-point threshold and modelled with Laplace-smoothed prior and likelihood probabilities. Nine independent binary Naïve Bayes classifiers were trained, one per field, and their posterior probabilities were subsequently passed to a Greedy Allocation procedure that enforces a fixed quota of two students per field. Evaluated against real ground-truth labels, the combined confusion matrix over all nine fields (360 decisions) yielded an overall accuracy of 90.28%, a macro-average precision of 84%, a macro-average recall of 65%, and a macro-average F1-score of 70%. The Greedy Allocation stage produced a final roster of 18 students, of which 14 (77.78%) matched the actual selection outcome. The findings indicate that the proposed system can support faster, more consistent, and auditable Olympiad selection, although recall for the minority "pass" class remains a limitation that warrants further attention.