Daily Container Volume Throughput Forecasting at Container Terminal Using Long-Short Term Memory (LSTM) Recurrent Neural Network
##plugins.themes.academic_pro.article.sidebar##
Published
Jun 30, 2025
Download
Statistic
Downloads
Download data is not yet available.
##plugins.themes.academic_pro.article.main##
Abstract
Container throughput is an important indicator for measuring the efficiency of a kontainer terminal. Kontainers that enter and exit are those transported to and from the terminal, respectively. Kontainers are stacked in the kontainer yard before they leave the terminal. Handling these kontainers accounts for a major workload at the terminal. Therefore, accurate short-term forecasting of daily kontainer gate-in and Gate-Out at a kontainer terminal is crucial for operational planning. While most forecasts are made at the strategic level of overall kontainer throughput, this study focuses on the daily kontainer gate-in and Gate-Out quantities with a case study at the TPKNM Makassar Kontainer Terminal. The study results show that the Epoch for each training set and performance metrics for each feature are 10, 50, and 100. Based on this, the difference in prediction performance with different epoch sizes is quite significant. The larger the Epoch, the smaller the MSE level.
##plugins.themes.academic_pro.article.details##
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Kasanah, Y. U., Miftahol Arifin, Famila Dwi Winati, & Fatbayani. (2025). Daily Container Volume Throughput Forecasting at Container Terminal Using Long-Short Term Memory (LSTM) Recurrent Neural Network. Jurnal E-Komtek, 9(1), 98-110. https://doi.org/10.37339/e-komtek.v9i1.2214
References
[1] R. Sains Dan Teknologi Kelautan, A. Nasser, and dan Marselinus Aril Patunggu, “ANALISA DAMPAK KINERJA PT. PELINDO PASCA DI-MERGER,” 2022.
[2] “Hambatan Bongkar-Muat Kapal dalam Kegiatan Ekspor-Impor di Pelabuhan Tanjung Priok Jakarta”, doi: 10.61722/jiem.v1i4.288.
[3] S. Shankar, P. V. Ilavarasan, S. Punia, and S. P. Singh, “Forecasting container throughput with long short-term memory networks,” Industrial Management and Data Systems, vol. 120, no. 3, pp. 425–441, Mar. 2020, doi: 10.1108/IMDS-07-2019-0370.
[4] A. Huang, K. K. Lai, H. Qiao, S. Wang, and Z. Zhang, “An interval knowledge based forecasting paradigm for container throughput prediction,” in Procedia Computer Science, Elsevier B.V., 2015, pp. 1381–1389. doi: 10.1016/j.procs.2015.07.126.
[5] J. van Twiller, A. Sivertsen, D. Pacino, and R. M. Jensen, “Literature survey on the container stowage planning problem,” Sep. 16, 2024, Elsevier B.V. doi: 10.1016/j.ejor.2023.12.018.
[6] H. Shin, C. Kim, and H. Kim, “[ICACE2022] Prediction of container vessel waiting time in Busan port using LSTM neural network,” Journal of Advanced Marine Engineering and Technology, vol. 46, no. 6, pp. 394–401, Dec. 2022, doi: 10.5916/jamet.2022.46.6.394.
[7] M. H. Ha, Z. Yang, and J. S. L. Lam, “Port performance in container transport logistics: A multi-stakeholder perspective,” Transp Policy (Oxf), vol. 73, pp. 25–40, Jan. 2019, doi: 10.1016/j.tranpol.2018.09.021.
[8] Y. Gao, D. Chang, T. Fang, and Y. Fan, “The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network,” J Adv Transp, vol. 2019, 2019, doi: 10.1155/2019/5764602.
[9] M. A. M. Saleh, A. Hicham, M. L. B. Maâti, H. Taha, and Y. M. A. Mohammed, “Development of a sustainable strategy model for predicting optimal container stacking locations in container yards using artificial intelligence and cubic data,” Kuwait Journal of Science, vol. 51, no. 2, Apr. 2024, doi: 10.1016/j.kjs.2023.100174.
[10] X. Song et al., “Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model,” J Pet Sci Eng, vol. 186, Mar. 2020, doi: 10.1016/j.petrol.2019.106682.
[11] D. Joseph, T. Gallege, E. T. Bekar, C. Dudas, and A. Skoogh, “A Predictive Maintenance Application for A Robot Cell using LSTM Model,” in IFAC-PapersOnLine, Elsevier B.V., Jul. 2022, pp. 115–120. doi: 10.1016/j.ifacol.2022.09.193.
[12] S. Krishna Lakshminarayanan and J. McCrae, “A Comparative Study of SVM and LSTM Deep Learning Algorithms for Stock Market Prediction,” 2019.
[13] N. Li, Z. Wang, X. Lin, and H. Sheng, “Prediction of delivery truck arrivals at container terminals: an ensemble deep learning model,” Maritime Economics and Logistics, 2024, doi: 10.1057/s41278-024-00304-1.
[14] M. Sangiorgio and F. Dercole, “Robustness of LSTM neural networks for multi-step forecasting of chaotic time series,” Chaos Solitons Fractals, vol. 139, Oct. 2020, doi: 10.1016/j.chaos.2020.110045.
[15] J. Jin, T. Cui, R. Bai, and R. Qu, “Container port truck dispatching optimization using Real2Sim based deep reinforcement learning,” Eur J Oper Res, vol. 315, no. 1, pp. 161–175, May 2024, doi: 10.1016/j.ejor.2023.11.038.
[16] E. Dave, A. Leonardo, M. Jeanice, and N. Hanafiah, “Forecasting Indonesia Exports using a Hybrid Model ARIMA-LSTM,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 480–487. doi: 10.1016/j.procs.2021.01.031.
[17] A. A. Pierre, S. A. Akim, A. K. Semenyo, and B. Babiga, “Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches,” Energies (Basel), vol. 16, no. 12, Jun. 2023, doi: 10.3390/en16124739.
[18] A. K. Mandal, R. Sen, S. Goswami, and B. Chakraborty, “Comparative study of univariate and multivariate long short-term memory for very short-term forecasting of global horizontal irradiance,” Symmetry (Basel), vol. 13, no. 8, Aug. 2021, doi: 10.3390/sym13081544.
[19] W. Hastomo, A. S. Bayangkari Karno, N. Kalbuana, A. Meiriki, and Sutarno, “Characteristic Parameters of Epoch Deep Learning to Predict Covid-19 Data in Indonesia,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Jun. 2021. doi: 10.1088/1742-6596/1933/1/012050.
[20] M. P. Putra and Y. Sibaroni, “Effect of Epoch Value on the Performance of the RNN-LSTM Algorithm in Classifying Lazada App Review Sentiments,” Jurnal dan Penelitian Teknik Informatika, vol. 8, no. 2, 2024, doi: 10.33395/v8i2.13368.
[2] “Hambatan Bongkar-Muat Kapal dalam Kegiatan Ekspor-Impor di Pelabuhan Tanjung Priok Jakarta”, doi: 10.61722/jiem.v1i4.288.
[3] S. Shankar, P. V. Ilavarasan, S. Punia, and S. P. Singh, “Forecasting container throughput with long short-term memory networks,” Industrial Management and Data Systems, vol. 120, no. 3, pp. 425–441, Mar. 2020, doi: 10.1108/IMDS-07-2019-0370.
[4] A. Huang, K. K. Lai, H. Qiao, S. Wang, and Z. Zhang, “An interval knowledge based forecasting paradigm for container throughput prediction,” in Procedia Computer Science, Elsevier B.V., 2015, pp. 1381–1389. doi: 10.1016/j.procs.2015.07.126.
[5] J. van Twiller, A. Sivertsen, D. Pacino, and R. M. Jensen, “Literature survey on the container stowage planning problem,” Sep. 16, 2024, Elsevier B.V. doi: 10.1016/j.ejor.2023.12.018.
[6] H. Shin, C. Kim, and H. Kim, “[ICACE2022] Prediction of container vessel waiting time in Busan port using LSTM neural network,” Journal of Advanced Marine Engineering and Technology, vol. 46, no. 6, pp. 394–401, Dec. 2022, doi: 10.5916/jamet.2022.46.6.394.
[7] M. H. Ha, Z. Yang, and J. S. L. Lam, “Port performance in container transport logistics: A multi-stakeholder perspective,” Transp Policy (Oxf), vol. 73, pp. 25–40, Jan. 2019, doi: 10.1016/j.tranpol.2018.09.021.
[8] Y. Gao, D. Chang, T. Fang, and Y. Fan, “The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network,” J Adv Transp, vol. 2019, 2019, doi: 10.1155/2019/5764602.
[9] M. A. M. Saleh, A. Hicham, M. L. B. Maâti, H. Taha, and Y. M. A. Mohammed, “Development of a sustainable strategy model for predicting optimal container stacking locations in container yards using artificial intelligence and cubic data,” Kuwait Journal of Science, vol. 51, no. 2, Apr. 2024, doi: 10.1016/j.kjs.2023.100174.
[10] X. Song et al., “Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model,” J Pet Sci Eng, vol. 186, Mar. 2020, doi: 10.1016/j.petrol.2019.106682.
[11] D. Joseph, T. Gallege, E. T. Bekar, C. Dudas, and A. Skoogh, “A Predictive Maintenance Application for A Robot Cell using LSTM Model,” in IFAC-PapersOnLine, Elsevier B.V., Jul. 2022, pp. 115–120. doi: 10.1016/j.ifacol.2022.09.193.
[12] S. Krishna Lakshminarayanan and J. McCrae, “A Comparative Study of SVM and LSTM Deep Learning Algorithms for Stock Market Prediction,” 2019.
[13] N. Li, Z. Wang, X. Lin, and H. Sheng, “Prediction of delivery truck arrivals at container terminals: an ensemble deep learning model,” Maritime Economics and Logistics, 2024, doi: 10.1057/s41278-024-00304-1.
[14] M. Sangiorgio and F. Dercole, “Robustness of LSTM neural networks for multi-step forecasting of chaotic time series,” Chaos Solitons Fractals, vol. 139, Oct. 2020, doi: 10.1016/j.chaos.2020.110045.
[15] J. Jin, T. Cui, R. Bai, and R. Qu, “Container port truck dispatching optimization using Real2Sim based deep reinforcement learning,” Eur J Oper Res, vol. 315, no. 1, pp. 161–175, May 2024, doi: 10.1016/j.ejor.2023.11.038.
[16] E. Dave, A. Leonardo, M. Jeanice, and N. Hanafiah, “Forecasting Indonesia Exports using a Hybrid Model ARIMA-LSTM,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 480–487. doi: 10.1016/j.procs.2021.01.031.
[17] A. A. Pierre, S. A. Akim, A. K. Semenyo, and B. Babiga, “Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches,” Energies (Basel), vol. 16, no. 12, Jun. 2023, doi: 10.3390/en16124739.
[18] A. K. Mandal, R. Sen, S. Goswami, and B. Chakraborty, “Comparative study of univariate and multivariate long short-term memory for very short-term forecasting of global horizontal irradiance,” Symmetry (Basel), vol. 13, no. 8, Aug. 2021, doi: 10.3390/sym13081544.
[19] W. Hastomo, A. S. Bayangkari Karno, N. Kalbuana, A. Meiriki, and Sutarno, “Characteristic Parameters of Epoch Deep Learning to Predict Covid-19 Data in Indonesia,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Jun. 2021. doi: 10.1088/1742-6596/1933/1/012050.
[20] M. P. Putra and Y. Sibaroni, “Effect of Epoch Value on the Performance of the RNN-LSTM Algorithm in Classifying Lazada App Review Sentiments,” Jurnal dan Penelitian Teknik Informatika, vol. 8, no. 2, 2024, doi: 10.33395/v8i2.13368.