Comparative Analysis of Machine Learning Classification and Clustering for Predicting PIP Eligibility
DOI:
https://doi.org/10.64366/ijids.v3i1.571Keywords:
Program Indonesia Pintar (PIP); Machine Learning; Classification; Clustering; Educational Data MiningAbstract
Accurate identification of students eligible for the Program Indonesia Pintar (PIP) is essential to support fair and effective distribution of educational financial assistance. Manual selection processes may be time-consuming and susceptible to inconsistent decision-making. This study comparatively evaluates five machine learning classification algorithms, Decision Tree, Random Forest, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression, for predicting PIP eligibility and additionally applies K-Means clustering to explore socioeconomic patterns among students. The dataset was obtained from SMA Negeri 1 Telaga Biru and consists of 1,092 student records containing demographic and socioeconomic attributes. Data preprocessing included missing-value handling, feature selection, Ordinal Encoding, undersampling, Min-Max normalization, and a 70:30 train–test split. Classification performance was evaluated using Accuracy, Precision, Recall, and F1-Score, while clustering quality was assessed using the Silhouette Score. Decision Tree and Random Forest achieved the highest classification performance, with an Accuracy of 92.38%, Precision of 92.10%, Recall of 99.26%, and F1-Score of 95.54%. K-Means produced a Silhouette Score of 0.4431, indicating a reasonably good clustering structure. The main contribution of this research is a comparative evaluation of five complementary classification approaches on PIP eligibility data from a senior high school, combined with K-Means-based socioeconomic pattern analysis. The results show that tree-based models provide strong decision-support performance, while clustering offers complementary insights into student characteristics. This framework can support more consistent, transparent, and data-driven PIP recipient selection.
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