Time Series Forecasting for Container Throughput Using SARIMA and LSTM: A Case Study of Tanjung Emas Port, Semarang

Abstract: Accurate forecasting of container throughput is vital for enhancing strategic planning and operational efficiency in seaport management. This study compares the performance of two time series forecasting models Seasonal Autoregressive Integrated Moving Average (SARIMA) and Long Short-Term Memory (LSTM) in predicting container throughput at Tanjung Emas Port, Semarang, Indonesia. Monthly throughput data from January 2014 to April 2025 were preprocessed using stationarity transformation and normalization techniques. Model performance was evaluated using Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The SARIMA model effectively captured seasonal patterns and produced accurate short-term forecasts. Conversely, the LSTM model exhibited notable significant deviation from the actual data , indicating lower predictive performance in this context. The findings indicate that SARIMA currently provides a more reliable forecasting approach for the port. Future research should consider hybrid models (e.g., SARIMA-LSTM) and incorporate exogenous variables to improve forecasting accuracy and support data-driven decision-making in port operations.