<![CDATA[The demand for Liquefied Petroleum Gas (LPG) in Indonesia continues to increase along with the growing population, economic activities, and energy conversion policies. On the other hand, the demand pattern for LPG is fluctuating and influenced by seasonal factors, making the distribution planning process difficult. Inaccuracy in forecasting LPG demand can lead to an imbalance between supply and demand, resulting in either supply shortages or stockpiling. This study aims to compare the performance of the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) methods in forecasting LPG demand using daily historical data from PT Pertamina Patra Niaga for the period from January 2023 to December 2025. The research stages include data preprocessing, stationarity testing using the Augmented Dickey-Fuller (ADF) test, model formation using Auto ARIMA, and evaluation using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Akaike Information Criterion (AIC). The research results show that the ARIMA (2,1,4) model produces an MAE value of 114,427.20, an RMSE value of 219,563.30, and an MAPE value of 55.48%, while the SARIMA (3,1,3) (1,0,0,7) model produces an MAE value of 114,663.22, an RMSE value of 219,532.32, and an MAPE value of 55.46%. Although the ARIMA model yields a slightly lower MAE value, the SARIMA model provides lower RMSE, MAPE, and AIC values, making it the best model in this study because it better represents seasonal patterns in the historical data. The research results indicate that the SARIMA (3,1,3) (1,0,0,7) model is more suitable for supporting short-term LPG distribution planning based on historical data.]]>
