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Rusdah Rusdah
Fakultas Teknologi Informasi Universitas Budi Luhur
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Journal : journal of applied informatics and computing

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A Forecasting Modeling of Imported Goods Release Waiting Time in Importer Logistics Operations Using Multiple Linear Regression Alfad Zebua, Vivid Kristiani; Rusdah, Rusdah
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9725

Abstract

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.
Co-Authors Achmad Saleh Achmad Solichin Ahadti Puspa Sari Ahmad Pudoli Alfad Zebua, Vivid Kristiani Andi Andara Andi Rukmana Anidnya Putri Pradiptha Anindya Sasi Kirana Anita Diana Anubhakti, Dian Ary Maulana Pratama Aryabima, Muhammad Iqbal Bregastantyo, Brian Agni Brury Trya Sartana David Jefri Aruan Deasy Aprilla Wulandari Deni Mahdiana Devit Setiono Dewi Kusumaningsih Diwi Apriana Dwi Achadiani Dwi Kristanto Dwi Pebrianti Elfy Susanti Ernita Rahayu Fauzan, Muhammad Rafi Fildza Izzati Firdhan Happyanda Hari Soetanto Haris Kurniawan Hin, Law Li Humisar Hasugian Ibnu Fallah Rosyadi Ilham Akbar Muharrom Ilyas, Aldrin Nur Imam Halim Mursyidin Indah Puspasari Handayani Indra Nugraha Irawati, Riri Izzati, Fildza Jan Everhard Riworuhi Joko Christian Chandra Joko Sutrisno Juliasari, Noni Kardena, Sucinda Kusumaningsih, Dewi Lauw Li Hin Linda Ratna Sari Lis Suryadi, Lis Luhur Bayuaji Lukas Umbu Zogara Lutfi Daniah Mahesworo Langgeng Wicaksono Marimin , Maulana Agung Saputro Mawarni, Ajeng Citra Mohammad Syafrullah Muhamad Satriadi Muhamad Sobirin Jamil Muhammad Fauzan Hadi Saputra Muhammad Rifqi Nurul Arifin Subandi Painem, Painem Patlisan, Patlisan Pudoli, Ahmad Purwanto Purwanto Putri, Ine Widyaningrum Mustama Raden Rahmad Rafi Naufal AlBasri Rahmat Fajar Rahmawati Alvira Rahmawati, Fadilla Salsabila Raissa, Benita Hasna Ratna Ujiandari Renaldi Setiawan Putra Ridha Mukhtar Ririt Roeswidiah Rizky Pradana, Rizky Rizq Mas Galih Wibisono Roeswidiah, Ririt Rohmad Atkha Setyawan Widyarto Shintya Yulianti Sri Hanafi Sri Wahyuningsih Sri Wahyuningsih Sucinda Kardena Supardi Supardi Susi Widyawati Syarifuddin Abdullah , Andi Tri Annisa Hidayati Triana Anggraini Umiyati, Hesti Wahiddin Ishak Yoshua Alfia Agatha Yulianawati Yulianawati Yulianawati Yuliazmi, Yuliazmi Yuni Kasmawati Zaqi Kurniawan