Passenger Density Prediction at the Airport Using LSTM and SARIMA: A Case Study at Radin Inten Airport, Lampung

Passenger density prediction at airports is a critical aspect of operational planning and strategic decision-making. This study aims to develop a passenger count prediction model for Radin Inten Airport in Lampung using a combination of Seasonal AutoRegressive Integrated Moving Average (SARIMA) and Long Short-Term Memory (LSTM), and to compare it with Random Forest and XGBoost models. The dataset consists of daily passenger counts from January 2023 to December 2024. The research includes data exploration, preprocessing, separate modeling with SARIMA and LSTM, and their integration through a residual learning approach. Evaluation results show that SARIMA achieved the best performance in capturing seasonal patterns with a Mean Absolute Percentage Error (MAPE) of 3.81%, followed by Random Forest with 5.81% and XGBoost with 5.84%. The LSTM model performed less effectively with a MAPE of 6.81%. Although the SARIMA–LSTM combination is theoretically promising, it produced a worse result with a MAPE of 14.27% due to error accumulation in the residual learning stage. This study highlights that the choice of prediction model strongly depends on data characteristics and forecasting objectives, as well as the importance of multi-model integration to improve prediction accuracy in airport passenger density forecasting applications.