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All Journal NEUTRON : Research from all areas of the Civil Engineering
Ifan Widayatullah
Narotama University
Civil Engineering, Building, Construction & Architecture Engineering Transportation

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ARTIFICIAL INTELLIGENCE FOR MARITIME SAFETY AND ENVIRONMENTAL SURVEILLANCE IN INDONESIA: AN OFFICIAL-STATISTICS BASELINE Ifan Widayatullah; Muhammad Ikhsan Setiawan; Mohd Haziman Wan Ibrahim
Neutron Vol 24 No 01 (2024): JULY 2024
Publisher : NAROTAMA UNIVERSITY, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29138/neutron.v24i01.298

Abstract

Maritime safety and environmental surveillance in Indonesia depend on the reliability of official data supporting artificial intelligence (AI). This study establishes an official-statistics baseline using BPS-Statistics Indonesia’s Marine and Coastal Resources Statistics 2023. It examines fishing-vessel fleets for 2021–2022 (34 provinces; 1,004,060 and 893,258 vessels), 118 classified fishing ports, 41 recorded oil-spill incidents (1999–2023), port-water monitoring at 27 sites and fisheries patrol records. Methods include concentration indices, k-means clustering with silhouette selection, principal component analysis, modified z-scores and Isolation Forest screening. In 2022, 88.8% of the recorded fleet was motorised; provincial distribution showed a Gini coefficient of 0.41. Three fleet typologies were identified: outboard-dominated, inboard-dominated and non-motorised-heavy. Ten of 94 provincial vessel-type changes were flagged as statistical outliers, while 10 of 27 water-monitoring sites reported none of three core parameters. In 2021, there was approximately one recorded inspection per 385 fishing vessels. These descriptive findings inform candidate AIS trajectory analytics, SAR oil-spill detection, optical satellite monitoring and smart-port planning. The study proposes a phased, data-first roadmap; it does not estimate the performance of operational AI systems in Indonesian waters.
BLOCKCHAIN-ENABLED SMART LOGISTIC SHIPS: ENHANCING SUPPLY CHAIN TRACEABILITY, ADMINISTRATIVE EFFICIENCY, AND CARBON COMPLIANCE IN INDONESIAN ARCHIPELAGIC MARITIME NETWORKS Ifan Widayatullah; Koespiadi Koespiadi; Derrick I-Hsien Ting
Neutron Vol 24 No 02 (2025): JANUARY 2025
Publisher : NAROTAMA UNIVERSITY, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29138/neutron.v24i02.301

Abstract

Indonesian inter-island freight logistics requires coordinated document exchange, cargo visibility, and credible operational carbon records. This study develops an evidence-audited conceptual architecture for blockchain-enabled smart logistic ships, integrating electronic bills of lading (eBL), onboard Internet-of-Things (IoT) telemetry, permissioned Hyperledger Fabric networks, and a carbon-data audit trail. Official Statistics Indonesia publications provide sectoral context; verified maritime blockchain studies, IMO rules, and the UNCITRAL Model Law on Electronic Transferable Records underpin the proposed architecture. The methodology specifies participant roles, off-chain sensor-data storage, on-chain event hashes, state transitions, authorization controls, and a reproducible cost-benefit framework. No prototype logs, port transaction records, or measured process times were supplied; therefore, the original document-latency, dwell-time, cost-saving, and return-on-investment percentages cannot be reported as verified empirical results. A transparent conditional illustration using author-provided cost assumptions is presented instead. The resulting contribution is a technically consistent, auditable design and validation protocol for future trials on Indonesian feeder routes, rather than a claim of demonstrated deployment performance.
ARTIFICIAL INTELLIGENCE AND PREDICTIVE ANALYTICS FOR SMART SEA TRANSPORTATION AND PORT ENVIRONMENTAL MANAGEMENT: AN INDONESIAN EVIDENCE-INFORMED FRAMEWORK Ifan Widayatullah; Muhammad Ikhsan Setiawan; Che Zalina Zulkifli
Neutron Vol 25 No 01 (2025): JULY 2025
Publisher : NAROTAMA UNIVERSITY, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29138/neutron.v25i01.306

Abstract

Indonesia’s archipelagic geography places considerable demands on marine transport, port environmental management and fisheries surveillance. This article develops an evidence-informed artificial intelligence (AI) framework linking public marine statistics to three proposed analytical functions: gradient-boosted water-quality prediction, recurrent time-series forecasting and vessel-trajectory anomaly screening. The study reviews the officially published BPS Marine and Coastal Resources Statistics 2023, related KKP fleet data and traceable peer-reviewed methods. Government-reported 2022 marine fishing craft totaled 893,258, while the 2022 fisheries production figures in a later BPS statistical compilation were approximately 7.49 million tonnes from capture fisheries and 14.78 million tonnes from aquaculture. The proposed four-layer architecture specifies data, preprocessing, analytics and human-validated decisions. Importantly, the submitted manuscript does not supply model code, station-level training data, out-of-sample predictions or patrol simulation logs; its claimed AI accuracy and fuel savings therefore cannot be independently validated. The contribution is a reproducible research design and evidence map rather than a demonstrated performance improvement.
Co-Authors Che Zalina Zulkifli Derrick I-Hsien Ting koespiadi koespiadi Mohd Haziman Wan Ibrahim Muhammad Ikhsan Setiawan