<![CDATA[Mass transportation, particularly Light Rail Transit (LRT), plays a crucial role in supporting the modern-era mobility of Palembang's community. With the city's transit-oriented growth and high population density, the increased utilization of LRT presents challenges, such as congestion during holidays and special events, necessitating effective solutions to anticipate changes in passenger numbers. This study aims to design and implement a prediction model using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method to anticipate and forecast the number of Palembang Light Rail Transit (LRT) passengers. By integrating seasonal components into time series analysis, this research focuses on developing a model that can accurately predict fluctuations in LRT passenger numbers, especially during special events or holiday seasons. The SARIMA method is expected to be an effective tool in public transportation management for planning operational sustainability and ensuring optimal services for the Palembang community. The prediction results using the parameter model (0,1,1) (0,1,0) obtained an RMSE value of 57.68 and a Mean Absolute Percentage Error (MAPE) value of 16.69%; thus, the accuracy level achieved is 83.31%."]]>
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