Forecasting Modeling of Imported Goods Release Waiting Time in Importer Logistics Operations Using Multiple Linear Regression
DOI:
https://doi.org/10.30871/jaic.v9i4.9725Keywords:
Import, Waiting Time, Multiple Linear Regression, Machine Learning, LogisticsAbstract
Import activities play a critical role in international trade, directly affecting logistics efficiency and the competitiveness of importing companies. The process of releasing imported goods at ports often involves complex administrative procedures that can cause delays, leading to increased logistics costs. This study aims to predict the waiting time for the release of imported goods using a machine learning approach. A case study was conducted at PT. Sentra Sarana Logistic, a licensed customs broker responsible for import administration. The primary model applied was Multiple Linear Regression (MLR), and its performance was compared with Neural Network (NN) and Support Vector Machine (SVM) algorithms. Several influencing factors were considered, including tax payment time, inspection duration, and inspection status. Evaluation results indicate that the MLR model achieved the best performance, with an RMSE of 0.00653, MAE of 0.00544, and R-squared of 0.99999, demonstrating high prediction accuracy and a strong linear correlation. The SVM model yielded acceptable results (RMSE 0.74107, R-squared 0.98388) but underperformed compared to MLR. The NN model showed the lowest accuracy with RMSE 2.86599, MAE 2.38831, and R-squared 0.69510. The findings suggest that MLR, despite its simplicity, is highly effective for predicting waiting times in import logistics operations. This research not only offers a practical decision-support tool for importers but also contributes to the existing literature on machine learning applications in logistics operations and customs processing.
Downloads
References
[1] S. Supartini, I. Iswanto, N. Astriawati, V. Dekanawati, and N. K. Hamzah Alfanzuri, “Pelayanan Jasa Impor Barang Dalam Masa Pandemi,” Dinamika Bahari, vol. 3, no. 2, pp. 114–123, Oct. 2022, doi: 10.46484/db.v3i2.313.
[2] I. Sani and S. Nanda Lubis, “Proses Pengurusan Dokumen Barang Ekspor Pada PT. Bahari Eka Nusantara Cabang Medan,” Journal of Maritime and Education (JME), vol. 4, no. 1, pp. 358–363, Feb. 2022, doi: 10.54196/jme.v4i1.70.
[3] Hotrahelita Girsang and Dafid Ginting, “Peranan Divisi Operasional dalam Kegiatan Ekspor Impor pada Pt.Prima Indonesia Logistik Belawan,” Journal Of Social Science Research, vol. 3, no. 6, pp. 9533–9551, 2023.
[4] R. K. D. B. M. K. Karjono1, “Kesiapan Perusahaan Pengurusan Jasa Kepabeanan (Ppjk) Dalam Kegiatan Impor Barang Pada PT. Terminal Intimoda Utama Cabang Semarang”.,” Refi, T.M. and Alhabsi, M., 2022. Prosedur Pengurusan Barang Impor Pada Kantor Pengawasan dan Pelayanan Bea Cukai Tipe Madya Pabean C Langsa Kota Langsa. , 1(1), pp.21-25., pp. 21–25, 2022.
[5] R. Sains, D. T. Kelautan, A. Ramadani, and C. Virandika, “Analisa Pengaruh Waktu Tunggu Kapal Terhadap Biaya Logistik Di Terminal Petikemas Makassar,” 2022.
[6] K. Verawati, H. Rahmayanti, W. Hadi, A. Costa, and S. Almira, “Faktor Penyebab Keterlambatan Pengeluaran Barang Impor Pada Lapangan Penumpukan Wilayah 2 Terminal Multipurpose PT Pelabuhan Tanjung Priok,” Jurnal Penelitian Transportasi Laut, vol. 24, no. 1, pp. 21–32, Sep. 2022, doi: 10.25104/transla.v24i1.2022.
[7] A. Lalita Ivana and H. Moetriono, “Optimasi Waktu Sandar Kapal Untuk Meningkatkan Kinerja Pelayanan Di Terminal Jamrud Pelabuhan Tanjung Perak Surabaya,” 2021.
[8] M. Hudzaifah and A. A. Rismayadi, “Peramalan Arus Lalu Lintas Berdasarkan Waktu Tempuh Dan Cuaca Menggunakan Metode Time Series Decomposition,” JURNAL RESPONSIF, vol. 3, no. 2, pp. 207–215, 2021. Available: http://ejurnal.ars.ac.id/index.php/jti
[9] F. Suprata, C. Natalia, and A. Sugioko, “Analysing the cause of idle time in loading and unloading operation at Indonesian international port container terminal: Port of Tanjung Priok case study,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, May 2020. doi: 10.1088/1757-899X/847/1/012090.
[10] N. Nurdin, F. Fajriana, M. Maryana, and A. Zanati, “Information System for Predicting Fisheries Outcomes Using Regression Algorithm Multiple Linear,” Journal Of Informatics And Telecommunication Engineering, vol. 5, no. 2, pp. 247– 258, Jan. 2022, doi: 10.31289/jite.v5i2.6023.
[11] U. Masruroh Kusman, A. Hamid, D. Candra Rini Novitasari, W. Dianita Utami, and I. Ariyanto Wijaya, “Optimasi Model Penugasan Berdasarkan Peramalan Layanan Kapal Tunda Di Pelabuhan Tanjung Perak Menggunakan Metode Backpropagation,” 2023.
[12] J. Setyanto and T. B. Sasongko, “Sentiment Analysis of Sirekap Application Users Using the Support Vector Machine Algorithm,” 2024. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
[13] A. D. A. E. S. F. E. R. Ahlan Syaeful Millah, “Analisis Data dalam Penelitian Tindakan Kelas,” Jurnal Kreativitas Mahasiswa, vol. 1, no. 2, pp. 140–153, 2023.
[14] Y. Christian, “Rental Price Prediction of Boarding Houses in Batam City Using Linear Regression and Random Forest Algorithms,” 2023. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
[15] E. Wijaya, M. Arief Soeleman, and N. Andono, “Comparative Performance Analysis of Optimization Algorithms in Artificial Neural Networks for Stock Price Prediction,” 2025. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
[16] R. F. Ramadhan and W. M. Ashari, “Performance Comparison of Random Forest and Decision Tree Algorithms for Anomaly Detection in Networks,” 2024. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
[17] B. J. Rana, N. A. Setiyanto, and M. Akrom, “Prediction of Corrosion Inhibitor Efficiency Based on Quinoxaline Compounds Using Polynomial Regression,” 2025. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
[18] A. Novebrian Maharadja, I. Maulana, and B. Arif Dermawan, “Penerapan Metode Regresi Linear Berganda untuk Prediksi Kerugian Negara Berdasarkan Kasus Tindak Pidana Korupsi,” 2021. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
[19] R. S. Ratih Yulia Hayuningtyas, “Implementasi Data Mining Dengan Algoritma Multiple Linear Regression Untuk Memprediksi Penyakit Diabetes,” Jurnal Teknik Komputer AMIK BSI, vol. 8, no. 1, pp. 40–44, Jan. 2022.
[20] M. M. Dewi, R. Andriani, and M. Nuraminudin, “Performance Analysis of the Item-Based Collaborative Filtering Model in Yogyakarta Tourism Recommendations,” 2025. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
[21] A. Dewantoro and T. B. Sasongko, “Comparison of LSTM Model Performance with Classical Regression in Predicting Gaming Laptop Prices in Indonesia,” 2024. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Vivid Kristiani Alfad Zebua, Rusdah Rusdah
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
