<![CDATA[Enhancing regional competitiveness is a critical factor in driving economic growth, investment, and community welfare. However, the utilization of Regional Competitiveness Index (Indeks Daya Saing Daerah/IDSD) data in Indonesia has largely been limited to ranking purposes, thus failing to provide specific, data-driven policy recommendations. This study aims to develop a Decision Support System (DSS) for regional competitiveness policy recommendations by combining machine learning and Explainable Artificial Intelligence (XAI) within a Design Science Research (DSR) framework. The dataset originates from provincial IDSD data spanning 2022–2025, encompassing 12 assessment pillars as predictor variables. Three regression algorithms were examined: Linear Regression, Random Forest, and XGBoost. A Variance Inflation Factor (VIF) analysis was conducted to verify the absence of severe multicollinearity among the predictor variables. Based on performance evaluation, XGBoost was selected as the final model due to its superior predictive performance and stability, yielding an R² of 0.8712 on the 2025 test data and a mean 5-fold cross-validation R² of 0.7723. To enhance model transparency, SHapley Additive exPlanations (SHAP) was employed. Interpretation results revealed that Innovation Capability (Pillar 12), Adoption of Information and Communication Technology (Pillar 3), and Market Size (Pillar 10) are the most influential factors affecting regional competitiveness scores. Building on these findings, the developed system delivers context-specific, priority policy recommendations through an interactive dashboard. This study demonstrates that the integration of XGBoost and XAI constitutes a more objective, transparent, and adaptive data-driven decision-making solution for supporting regional competitiveness improvement in Indonesia.]]>
