<![CDATA[Domestic tourist travel is an important indicator of the development of Indonesia's domestic tourism sector. Predicting the number of domestic tourist trips is essential to support data-driven planning and decision-making. This study aims to develop a prediction model for domestic tourist travel based on historical data using an Artificial Neural Network (ANN). Secondary data obtained from Statistics Indonesia (BPS) were processed through preprocessing, Min-Max normalization, sliding window pattern formation, and ANN training using the Backpropagation algorithm. Model performance was evaluated using MAE, MSE, RMSE, MAPE, and R². The results obtained MAE of 0.0582, MSE of 0.0066, RMSE of 0.0813, MAPE of 9.44%, and R² of 0.3665, indicating that the proposed ANN model is capable of providing reasonably accurate predictions of domestic tourist travel based on historical data.]]>
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