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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic JOIN (Jurnal Online Informatika) Jurnal Ilmiah FIFO Informatics for Educators and Professional : Journal of Informatics MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL PENDIDIKAN TAMBUSAI JURTEKSI Jurnal Pengabdian Al-Ikhlas JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Ekonomi Manajemen Sistem Informasi Dinasti International Journal of Digital Business Management Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Ilmu Manajemen Terapan (JIMT) Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Jaring SainTek Journal of Students‘ Research in Computer Science (JSRCS) Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Journal of Accounting and Finance Management (JAFM) Digital Transformation Technology (Digitech) Jurnal Keamanan Nasional Journal of Computer Science Contributions (Jucosco) Journal of Informatics and Information Security Jurnal Psikologi Atribusi: Jurnal Pengabdian Masyarakat Dinasti Information and Technology
Adi Muhajirin
Bhayangkara Jakarta University
Religion Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Nursing Public Health Social Sciences Other

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Journal : Dinasti Information and Technology

 1 
Komparasi Metode LSTM dan Random Forest dalam Prediksi Waktu Sandar Kapal untuk Optimasi Alokasi Dermaga: Studi Kasus Pelabuhan Tanjung Pandan Andy Achmad Hendharsetiawan; Muhajirin, Adi; Alwi Rina Riyanto
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.v3i2.2929

Abstract

Efisiensi operasional pelabuhan sangat bergantung pada akurasi prediksi waktu sandar kapal, terutama di Pelabuhan Tanjung Pandan yang memiliki karakteristik tramp trade dengan variasi kapal yang tinggi. Penelitian ini bertujuan membandingkan kinerja metode Long Short-Term Memory (LSTM) dan Random Forest dalam memprediksi durasi sandar kapal sebagai dasar optimasi alokasi dermaga. Menggunakan data operasional periode 2023–2024 (125 observasi), variabel input mencakup Gross Tonnage (GT), Length Overall (LOA), serta tanggal tiba dan berangkat; sedangkan output adalah durasi sandar dalam jam. Data diproses melalui pembersihan, rekayasa fitur, dan normalisasi, lalu dibagi menjadi 80% latih dan 20% uji. Evaluasi dilakukan menggunakan RMSE, MAE, dan R². Hasil menunjukkan bahwa Random Forest mengungguli LSTM dengan RMSE 5,34 jam (vs. 7,82), MAE 4,07 jam (vs. 5,91), dan R² 0,917 (vs. 0,812), mengindikasikan kemampuannya menangkap interaksi non-linear antarfitur statis seperti GT dan LOA lebih efektif dalam konteks operasional pelabuhan ini. Temuan ini merekomendasikan penerapan Random Forest sebagai model prediktif dalam sistem pendukung keputusan alokasi dermaga untuk meningkatkan efisiensi dan mengurangi waiting time kapal
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 Achmad Mujib Ahmad Ajif Yunizar Pratama Yusuf Alpi Surya Perdana Alwi Rina Riyanto Anak Agung Istri Sri Wiadnyani Andy Achmad Hendharsetiawan Anggi Suwarno Anggraeni Silvia Azmi Mutawalli, Khoirul Budi Sarasati Dani Yusuf Dhuni Thamrin Dwi Budi Srisulistiowati Dwipa Handayani Esa Meila Fahmi Rizal Fried Sinlae Hapzi Ali Hapzi Ali Harry Priambodo Hendharsetiawan, Andy Achmad Herlawati Herlawati Hutagalung, Eka Putri Christiani Ida Oetari Poernamasari Izdihar, Zalfa Joni Setiawan Kardinah Indriana Meutia Khairunnisa Fadhilla Ramdhania Khalida, Rakhmi Khoirul Azmi Mutawalli Krispinus Nathaniel Laudza Maulidia, Puput Yuniar Moch Alfitho Mochamad Adhari Adiguna Muamar, Yasir MUHAMAD Muhamad Zaky Muhammad Allum Istifahany Muhammad Hisyam Muhammad Khoziali Muhammad Yasir Muhammad Yasir Mujib, Achmad Mukhlis Mukhlis Mukhlis Mukhlis Mukhlis Nabiilah Khoirunnisaa Nikmah Obie Jagad Prakoso Oetari Poernamasasi, Ida Perdana, Alpi Surya Puput Yuniar Maulidia Rafika Sari Rahman Irsyadi, Abdur Rakhmi Khalida Rony, Zahara Tussoleha Sarasati, Budi Sastro Atmojo Sasosno Silvia, Anggraeni SITI SETIAWATI SUGENG SUROSO, SUGENG Sumaryono Thamrin, Dhuni Tri Widyastuti Tri Widyastuti Truly Wangsalegawa Tumbur Togu Tyastuti Sri Lestari Verdi Ganda Wicaksono, Wisnu Wisnu Suhartanto S.P Yusuf, Ajif Yunizar Pratama Zia Tabaruk Zidan Fathul Wathon