Classification of Village Development Status in Bekasi Regency Using Ensemble Learning and SMOTE-Based Class Balancing
DOI:
https://doi.org/10.34123/jurnalasks.v18i1.870Keywords:
Random Forest, SMOTE, Village Potential Statistics (PODES), Village Development Index (IDM), Classification, Machine Learning, Ensemble Learning, Bekasi RegencyAbstract
Introduction/Main Objectives: This study aims to classify village development status in Bekasi Regency using machine learning based on the 2024 Village Potential Statistics (PODES) and the Village Development Index (IDM). Background Problems: Conventional descriptive assessments ignore complex socio-economic relationships, and class imbalance further reduces model predictive performance. Novelty: This study integrates PODES data, ensemble learning, and SMOTE to improve classification, providing a reliable, data-driven framework for village profiling and planning. Research Methods: Following preprocessing and a 70:30 split, SMOTE was applied to the training data, and four tree-based models (Decision Tree, Bagging, Random Forest, XGBoost) were evaluated using standard classification metrics. Finding/Results: The Random Forest model combined with SMOTE achieved the best classification performance, with an accuracy of 0.7778 and consistently high AUC values across all classes. The most influential predictors were the dominant economic sector, number of farmer groups, availability of basic health services, and presence of micro-business units. These findings demonstrate that combining ensemble learning with SMOTE improves village development classification and provides valuable support for evidence-based rural development planning in Bekasi Regency.
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