Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Maritime Park: Journal Of Maritime Technology and Socienty
Ratmoko, Hari
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Time Series Forecasting for Container Throughput Using SARIMA and LSTM: A Case Study of Tanjung Emas Port, Semarang Ratmoko, Hari; Zuliarso, Eri
Maritime Park: Journal of Maritime Technology and Society Volume 4, Issue 3, 2025
Publisher : Department of Ocean Engineering, Faculty of Engineering, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62012/mp.vi.45866

Abstract

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.
Co-Authors Eri Zuliarso