Kerangka Hybrid Adaptif SVR–PSO untuk Prediksi Tingkat Pengangguran Terbuka
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1316Keywords:
Adaptive Hybrid SVR–PSO; Data Panel; Particle Swarm Optimization; Support Vector Regression; Open Unemployment RateAbstract
Accurate prediction of the Open Unemployment Rate (OUR) is essential for supporting evidence-based employment policies; however, conventional prediction models often struggle to capture nonlinear relationships within panel socioeconomic data. This study proposes an Adaptive Hybrid Support Vector Regression–Particle Swarm Optimization (SVR–PSO) Framework for panel-based unemployment prediction in Central Java. The framework was evaluated using socioeconomic panel data integrating cross-sectional and temporal dimensions, consisting of 315 observations from 35 regencies and cities over the 2017–2025 period. Socioeconomic indicators, including labor force participation, education, minimum wage, human development, regional economic output, poverty, population density, population, and population growth, were used as predictor variables. Particle Swarm Optimization was employed to optimize the SVR hyperparameters, while prediction performance was assessed using MAE, RMSE, MAPE, and R². The proposed framework achieved an MAE of 0.7715, RMSE of 0.9264, MAPE of 16.78%, and R² of 0.6956, outperforming the baseline SVR model by reducing MAE, RMSE, and MAPE by 17.77%, 22.33%, and 11.31%, respectively, while increasing R² by 40.41%. These results demonstrate that the proposed Adaptive Hybrid SVR–PSO Framework improves predictive accuracy and model generalization across heterogeneous socioeconomic panel observations, providing a robust approach for panel-based unemployment prediction and supporting data-driven regional labor market planning.
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