<![CDATA[Forecasting volatile domestic gold prices requires a predictive model that is not only accurate but also transparent. Although the Long Short-Term Memory (LSTM) architecture excels at capturing temporal dependencies in time-series data, its performance is highly sensitive to hyperparameter tuning and suffers from a black-box nature. To overcome the limitations of manual optimization and lack of transparency, this study proposes a hybrid multivariate PSO-LSTM forecasting model integrated with the SHapley Additive exPlanations (SHAP) method. Built upon the CRISP-DM framework using daily historical data from 2020 to 2026, the Particle Swarm Optimization (PSO) algorithm is employed to automatically optimize the number of neurons, learning rate, and batch size. Furthermore, the SHAP method with K-Means background reduction is applied to map the contribution of input features. Experimental results demonstrate that the PSO-LSTM model outperforms the baseline LSTM by reducing Root Mean Squared Error (RMSE) by 11.70% and Mean Absolute Error (MAE) by 14.08%, while achieving a highly accurate Mean Absolute Percentage Error (MAPE) of 2.62%. Statistical testing using the Diebold-Mariano test confirms that the predictive superiority of the PSO-LSTM model is statistically significant. SHAP analysis indicates that historical memory of domestic gold prices provides the highest marginal contribution to model predictions, followed by USD/IDR exchange rate adjustments and global gold price sentiment. Overall, this study contributes an integrated financial modeling framework that not only enhances accuracy on highly volatile data but also provides rational feature interpretability as a foundation for hedging decisions by investors and policymakers.]]>
