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All Journal International Journal of Marine Engineering Innovation and Research
Akhmad Syarief
Departement of Mechanical Engineering, Universitas Lambung Mangkurat, Banjarmasin, 70123, Indonesia.
Automotive Engineering Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

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A Machine Learning-Based Approach for Designing SEEMP on Ships: Case Study of CO₂ Emissions at a Container Port Elwas Cahya Wahyu Pribadi; Abdul Ghofur; Rachmat Subagyo; Ma’ruf; Akhmad Syarief; Aldinor Setiawan
International Journal of Marine Engineering Innovation and Research Vol. 10 No. 4 (2025)
Publisher : Department of Marine Engineering, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25481479.v10i4

Abstract

The management of ship energy significantly affects both cost efficiency such as earnings before interest and environmental sustainability, particularly in reducing CO₂ emissions produced by ship operations. Despite the importance of this issue, research on ship energy consumption within container terminals remains limited. This study aims to estimate CO₂ emissions generated by ship activities in container ports, focusing on emission variables related to the Ship Energy Efficiency Management Plan (SEEMP). The calculation considers active ship movements in the port, including approach, maneuvering, and berthing processes. Energy consumption and CO₂ emissions were analyzed using random forest regression (RF) with default settings, and the model’s accuracy was validated through k-fold cross-validation. The results identified five major factors influencing CO₂ emissions: (1) main engine power, (2) auxiliary engine power, (3) waiting time in the port, (4) maneuvering time, and (5) berthing time. Among these, maneuvering, waiting, and berthing showed the highest significance, confirmed by attribute selection and validation results. The random forest model demonstrated a prediction accuracy of 98.89%, confirming its reliability. Moreover, operational fuel efficiency analysis indicated that combining voyage optimization, skilled operators, and cold ironing facilities could reduce CO₂ emissions by up to 20%. These findings provide valuable insights and serve as a foundation for developing a more effective Ship Energy Efficiency Management Plan to enhance environmental performance in maritime operations.
Co-Authors Abdul Ghofur Aldinor Setiawan Elwas Cahya Wahyu Pribadi Ma’ruf Rachmat Subagyo