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All Journal Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Manajemen Informatika dan Bisnis Digital Green Engineering: Journal of Engineering and Applied Science
Ronald Simanjuntak
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering

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Enhancing Port Security and Predictive Maintenance with IoT: Cadets' Perspectives Susi Herawati; Rosna Yuherlina Siahaan; April Gunawan Malau; Derma Watty Sihombing; Boedojo Wiwoho Soetatmoko Jogo; Ronald Simanjuntak
TEMATIK Vol. 11 No. 1 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i1.1881

Abstract

This research explores the perspectives of 100 cadets studying multimodal transportation on integrating Internet of Things (IoT) to enhance port security and predictive maintenance. Using qualitative methods, including interviews and document analysis, the study investigates the effectiveness of IoT in real-time monitoring, data security, and predictive maintenance. The findings highlight cadets' recognition of IoT's potential to transform port operations, particularly in improving security measures and maintenance strategies. Cadets also emphasize the importance of professionalism and adherence to standards in IoT integration, valuing compliance with standards, understanding of IoT, training and education, and ethical considerations. The research underscores the need for continuous education and training programmes to prepare future industry professionals for the challenges and opportunities presented by IoT technologies. Overall, this study contributes to the advancement of knowledge in transportation management and education, offering insights for policymakers, industry practitioners, and educators on the integration of IoT in port operations.
Digital Learning Management Systems for Maritime Decarbonization Training: An Adaptive Framework for Seafarer Competency Development Tri Kismantoro; Ronald Simanjuntak; Muhammad Nurdin; Nafi Almuzani; Ardiansyah, Ardiansyah
JUMINTAL: Jurnal Manajemen Informatika dan Bisnis Digital Vol. 4 No. 1 (2025): Mei 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jumintal.v4i1.6177

Abstract

The International Maritime Organization's 2023 Greenhouse Gas Strategy necessitates rapid workforce transformation to train 1.2 million seafarers in decarbonization competencies by 2050. This qualitative research investigates adaptive digital learning management systems for maritime decarbonization training through analysis of perspectives from five maritime education professionals with specialized expertise in green shipping technologies and IMO-based learning frameworks. The study employs descriptive analysis to examine participant insights regarding intelligent tutoring systems, predictive learning models, and digital assessment frameworks. Results demonstrate strong support for adaptive learning implementation, with an overall effectiveness score of 4.2 out of 5.0 and projected competency development improvements of 63.0% across domains. Thematic analysis reveals five critical dimensions: personalization imperative, real-time assessment integration, scalability solutions, implementation complexity, and regulatory alignment confidence. Adaptive learning systems can potentially train 240,000 seafarers annually compared to 2,400 through traditional methods while maintaining regulatory compliance. Findings contribute to adaptive learning theory and provide implications for maritime institutions.
Development of Terminal and Ship Operational Integration System for Docking and Berthing Time Optimization Based on Historical Data Irfan Faozun; Larsen Barasa; Natanael Suranta; Ronald Simanjuntak; Imam Fachruddin
Green Engineering: International Journal of Engineering and Applied Science Vol. 3 No. 1 (2026): January: Green Engineering: International Journal of Engineering and Applied Sc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v3i1.262

Abstract

This research investigates the development of integrated operational systems connecting terminal and ship operations for docking and berthing time optimization through systematic analysis of historical data. Port efficiency depends critically on minimizing vessel turnaround time, with berth allocation, docking procedures, and cargo operations coordination determining overall port productivity and competitiveness. Through qualitative analysis involving port operators, terminal managers, ship agents, harbor masters, and operations research specialists, this study examines how historical operational data can inform intelligent coordination systems improving berthing efficiency. Results demonstrate that data-driven integration systems incorporating predictive analytics, automated scheduling, and coordinated workflows can reduce average berth turnaround time by 15-30%, improve berth utilization by 20-35%, and decrease operational conflicts by 40-60% through optimized allocation and proactive coordination. Key implementation challenges include data quality and availability, system integration complexity, organizational coordination barriers, and resistance to automated decision support. Findings reveal that historical data-based optimization represents transformative advancement from experience-based scheduling to evidence-driven operational planning supporting port efficiency enhancement, capacity maximization, and service reliability improvement. This research contributes to port operations literature by providing practical frameworks for data-driven berthing optimization applicable to diverse port operational contexts.
Co-Authors April Gunawan Malau ARDIANSYAH ARDIANSYAH Barasa, Larsen Boedojo Wiwoho Soetatmoko Jogo Derma Watty Sihombing Imam Fachruddin Irfan Faozun Muhammad Nurdin Nafi Almuzani Natanael Suranta Rosna Yuherlina Siahaan Susi Herawati Tri Kismantoro