<![CDATA[The Human Development Index (HDI) is a key indicator for measuring regional development performance and serves as an essential reference for evidence-based policy formulation. Accurate HDI prediction is crucial to support effective development planning and decision-making. This study aims to develop an HDI prediction model using Support Vector Regression (SVR) optimized with Particle Swarm Optimization (PSO) to improve prediction accuracy. The dataset was obtained from Statistics Indonesia (BPS), covering 38 provinces during the 2015–2025 period with a total of 421 observations. The research process consisted of data preprocessing, Min-Max Scaling normalization, an 80:20 train-test split, SVR model development, parameter optimization using PSO, and performance evaluation based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The results show that the baseline SVR model achieved an MAE of 0.069187, RMSE of 0.093548, and R² of 0.492454. After PSO optimization, the model performance improved, achieving an MAE of 0.060864, RMSE of 0.084224, and R² of 0.588583. These findings demonstrate that PSO effectively enhances the predictive performance of SVR by identifying optimal parameter combinations. The main contribution of this study is the development and validation of an optimized SVR-PSO framework for HDI prediction using multi-provincial socioeconomic data in Indonesia, providing a more accurate machine learning-based approach to support data-driven human development planning and policy formulation.]]>
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