<![CDATA[The development of rail transportation in Indonesia continues to change over time. These changes are influenced by various factors, such as government policies, the economic situation, and improvements in railway infrastructure. This dynamic suggests that better transportation planning requires predictive techniques that can accurately identify changing patterns. This study aims to compare Linear Regression and Artificial Neural Network (ANN) methods in predicting national rail passenger numbers. Before being used for modeling, the time series data underwent a preprocessing stage. The research process included dividing the data into training and test data, applying both prediction methods, and evaluating model performance using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The results showed that the ANN method was more accurate than the Linear Regression method. Therefore, the ANN method may be a better choice to assist rail transportation planning in Indonesia.]]>
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