<![CDATA[The manual classification of Uang Kuliah Tunggal (UKT) groups at Indonesian public universities is laborious, subjective, and error-prone, especially given the explosion of socio-economic data captured via online admission portals. In this study, we evaluate five feature selection techniques Chi-Square filter, Random Forest importance, Recursive Feature Elimination, LASSO embedded selection, and Exploratory Factor Analysis on a dataset of 9,369 applicants described by 53 socio-economic variables. Six classifiers (Decision Tree, Random Forest, SVM-RBF, K-Nearest Neighbor, and Naïve Bayes) were tuned via stratified 5-fold cross-validation within an 80:20 train-test split. Performance was measured by accuracy, macro-F1, and training time, and differences in weighted-average accuracy across feature-selection scenarios were assessed using the Friedman test (χ² = 15.06, p = 0.010). Results show that reducing to 13 features via LASSO (weighted-average accuracy 0.730) or Chi-Square (0.678) significantly outperforms both the full feature baseline (0.624) and the EFA baseline (0.303), while cutting computational costs by over 40%. We conclude that supervised feature selection particularly LASSO and Chi-Square enables simpler, faster, and more transparent UKT prediction without sacrificing accuracy. The novelty of this study lies in comparing five feature-selection methods within a standardized preprocessing pipeline on real UKT data from UNESA, resulting in a 13-feature subset aligned with the current UKT policy. This finding is ready to be integrated into an automated UKT verification system to enhance decision accuracy and efficiency.]]>
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