<![CDATA[This research develops a K-Nearest Neighbors (KNN)-based classification model to determine the eligibility of students for Tuition Assistance (UKT) based on socio-economic factors, including parental income, family size, parental occupation, number of dependents, and housing conditions. The goal is to automate the process of identifying students eligible for financial aid, enhancing both the efficiency and fairness in resource allocation. The model was trained using a dataset consisting of both categorical and numerical features, with the target variable being binary: "Eligible" (1) or "Not Eligible" (0) for UKT relief. The KNN model achieved an overall accuracy of 92%, with strong performance in predicting the "Eligible" class. However, the "Not Eligible" class showed lower performance, particularly in terms of recall and F1-score, suggesting the presence of class imbalance. To address this issue, techniques such as class balancing, resampling, or adjusting KNN parameters are suggested to improve the model's ability to correctly classify minority instances. Additionally, exploring ensemble methods like Random Forest or XGBoost may provide more robust results. This study highlights the importance of addressing class imbalance and using appropriate evaluation metrics beyond accuracy when building classification models for imbalanced datasets.]]>
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