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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic JSI (Jurnal sistem Informasi) Universitas Suryadarma JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Manajemen Informatika Jayakarta INTERNATIONAL JOURNAL OF EDUCATION, INFORMATION TECHNOLOGY, AND OTHERS International Journal Science and Technology (IJST) Moneter : Jurnal Keuangan dan Perbankan Journal of Computer Science Contributions (Jucosco) Dinasti Information and Technology
Hendharsetiawan, Andy Achmad
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Social Sciences

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Journal : Dinasti Information and Technology

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Predicting Vessel Departure Delays at Tanjung Pandan Port Using Supervised Machine Learning : A Comparative Study of Logistic Regression, Decision Tree, and SVM Muhajirin, Adi; Hendharsetiawan, Andy Achmad; Mukhlis, Mukhlis
Dinasti Information and Technology Vol. 3 No. 2 (2025): Dinasti Information and Technology (October - December 2025)
Publisher : Dinasti Research & Yayasan Dharma Indonesia Tercinta (DINASTI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dit.v3i2.2943

Abstract

Operational delays in vessel departure disrupt maritime logistics and increase port dwell time. This study develops predictive models to anticipate departure delays at Tanjung Pandan Port using supervised machine learning. Three algorithms—Logistic Regression, Decision Tree, and Support Vector Machine (SVM)—were trained on 112 verified port calls (2023–2024) with key features: arrival date, scheduled departure date, vessel ownership status (milik vs. keagenan), and document response time. Delay was defined as exceeding the median turnaround time of 58 hours. Data preprocessing included imputation, time-difference engineering (e.g., ΔTIBA–BERANGKAT, response latency), and SMOTE for class balancing. Performance was evaluated using accuracy, precision, recall, and F1-score via 5-fold cross-validation. The Decision Tree model achieved the highest F1-score (0.86) and recall (0.89), identifying response latency > 12 hours, keagenan status, and arrival during neap tide windows as top predictors. SVM showed robust precision (0.88), while Logistic Regression offered the best interpretability of coefficient impact. The models collectively support proactive scheduling interventions-e.g., digital clearance acceleration or priority berthing for high-risk vessels—to mitigate delays. This study contributes the first ML-based delay prediction framework for shallow-draft, tramp-operated Indonesian ports.
Co-Authors Adi Muhajirin Agus Sugiharto Aida Fitriyani, Aida Al Din, Sayyid Jamal Dadan Irwan Dedi Setiadi Dwipa Handayani Ekawati, Inna Febrianto, Dannie Fried Sinlae Haryono Haryono Herlawati Herlawati Hutahaean, Johanes Andi Lawrence Khaerudina, Muhammad Khalida, Rakhmi Lubis, Hendarman Malikus Sumadyo Maulana, Muhammad Ismam Muhammad Khaerudin Muhammad Yasir Muhammadi, Noor Mukhlis Mukhlis Pamungkas, R. Wisnu Prio Permadi, Noor Prima Dina Atika Rafika Sari Rahmadya Trias Handayanto Ramdhania, Khairunnisa Fadhilla Retno Nugroho Whidhiasih Saputra, Ikbal Eka Tata Sumitra