An Explainable Spatio-Temporal Decision Support System (DSS) Using XGBoost And SHAP For Urban Complaint Trend Prediction - Sakmar, Moeng; Darmawan, Agus; Shofo, Puteri Awaliatus; Kadir, Nurul Tiara (Department of Informatics Universitas Islam Indonesia, 2026)
The increase in the volume of public complaints in urban areas requires an accurate and explainable decision support system. This study developed an Explainable Decision Support System (xDSS) based on the Extreme Gradient Boosting (XGBoost) algorithm combined with the SHapley Additive Explanations (SHAP) method to predict spatial and temporal trends in public complaints in DKI Jakarta Province. The research data was obtained from the Satu-Data Jakarta portal and included multi-year complaint reports that were processed through aggregation, temporal feature engineering, and regression-based metric evaluation. The results show that the XGBoost model has high predictive performance with an R² value of 0.8425, MAE of 2.9858, and RMSE of 4.9928, indicating the model’s ability to explain more than 84% of the variation in the actual number of complaints. SHAP analysis revealed that temporal features such as complaint_lag1 and complaint_ma3 had the most dominant influence, while external variables such as rainfall (rainfall_mm) and population density (population_density) also made positive contributions. These results indicate that the dynamics of public complaints are influenced by a combination of historical factors and environmental conditions. Practically, this xDSS system can provide accurate predictions and transparent interpretations, thereby supporting the implementation of Smart Governance and evidence-based policy. This approach strengthens the application of Explainable Artificial Intelligence (XAI) in public service governance by providing accurate, ethical, and auditable models to support strategic decision-making in the era of digital government.