<![CDATA[The Indonesia Smart College Card (Kartu Indonesia Pintar-Kuliah / KIP-K) is a national initiative aimed at expanding access to higher education for students from socioeconomically disadvantaged backgrounds. This study, conducted at Politeknik Negeri Cilacap, investigates the prediction of KIP-K acceptance based on the type of high school attended by applicants. A comparative analysis was carried out using three supervised machine learning algorithms: Extreme Gradient Boosting (XGBoost), Random Forest, and Support Vector Machine with Radial Basis Function (SVM-RBF). The dataset, sourced from institutional admission records between 2022 and 2024, comprises information on school types (public, private, vocational, madrasah, and others), demographic attributes, and the KIP-K acceptance status. The data were split into training and testing sets using a 50:50 stratified sampling technique to preserve class distribution. Model performance was evaluated using standard classification metrics, including accuracy, precision, recall, and F1-score. Additionally, confusion matrices, ROC curves, and feature importance visualizations were used to enhance model interpretability. The experimental results demonstrate that the XGBoost algorithm consistently outperformed the other models across all performance metrics. Specifically, XGBoost exhibited the highest discriminatory power with an AUC of 0.93, followed by Random Forest (0.90) and SVM-RBF (0.85). These findings affirm the suitability of tree-based ensemble methods for classification tasks in educational domains and emphasize the predictive relevance of school type in determining KIP-K eligibility. The study presents a data-driven decision support framework that can contribute to more objective, transparent, and equitable scholarship allocation practices, particularly within the context of vocational higher education institutions in Indonesia]]>
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