DOI: https://doi.org/10.14710/kapal.v21i1.58868

The necessity of implementing AI for enhancing safety in the Indonesian passenger shipping fleet

Shinta J.A. Rahadi  -  Research Center of Hydrodynamics Technology, National Research and Innovation Agency Republic of Indonesia (BRIN), Surabaya, Indonesia 60111, Indonesia
Dimas Fajar Prasetyo  -  Research Center of Hydrodynamics Technology, National Research and Innovation Agency Republic of Indonesia (BRIN), Surabaya, Indonesia 60111, Indonesia
*Muhammad Luqman Hakim  -  Department of Naval Architecture, Faculty of Engineering, Universitas Diponegoro, Semarang, Indonesia 50275, Indonesia
Dian Purnama Sari  -  Research Center of Hydrodynamics Technology, National Research and Innovation Agency Republic of Indonesia (BRIN), Surabaya, Indonesia 60111, Indonesia
Putri Virliani  -  Research Center of Hydrodynamics Technology, National Research and Innovation Agency Republic of Indonesia (BRIN), Surabaya, Indonesia 60111, Indonesia
Cakra W.K. Rahadi  -  Department of Naval Architecture, Ocean & Marine Engineering, Faculty of Engineering, University of Strathclyde, Glasgow, United Kingdom, United Kingdom
Rina Rina  -  Research Center of Hydrodynamics Technology, National Research and Innovation Agency Republic of Indonesia (BRIN), Surabaya, Indonesia 60111, Indonesia
R. D. Yulfani  -  Research Center of Hydrodynamics Technology, National Research and Innovation Agency Republic of Indonesia (BRIN), Surabaya, Indonesia 60111, Indonesia
Luthfansyah Mohammad  -  Automation Engineering Technology Study Program, Vocational School, Universitas Diponegoro, Semarang, Indonesia 50275, Indonesia
Diva Kurnianingtyas  -  Department of Informatics Engineering, Faculty of Computer Science, Universitas Brawijaya, Malang, Indonesia 65145, Indonesia
Open Access Copyright (c) 2022 Kapal: Jurnal Ilmu Pengetahuan dan Teknologi Kelautan
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
The shipping industry, grappling with escalating challenges, increasingly adopts Artificial Intelligence (AI) to enhance efficiency, safety, and environmental impact. Experts endorse ship automation and AI implementation for safety, navigation, and operational efficiency in ferry networks. This paper underscores AIS technology's role in maritime safety and environmental protection, emphasizing AI's potential in navigation and knowledge gap bridging. Indonesia, with its numerous islands and significant population, faces complex challenges in ensuring safe maritime transportation. Collaborative efforts among the government, industry, and stakeholders are vital for enhancing safety standards across the archipelago. Despite regulations, Indonesia contends with a high ferry accident rate, prompting the need for preventive measures. The study reviews AI's application in preventing sea accidents, recognizing its contributions and potential effectiveness in maritime safety. Acknowledging challenges like data quality and cybersecurity, the paper emphasizes the necessity of AI development for passenger ship safety. It concludes by highlighting significant research efforts, endorsing AI's promising role in reshaping the industry for improved efficiency and safety. Further exploration of AI applications, particularly in passenger ship safety, is recommended to meet evolving challenges in the maritime sector.
Fulltext View|Download
Keywords: Artificial Intelligence; Passenger Ship; Maritime Safety; Risk Mitigation
Funding: RISPRO-LPDP Ministry of Finance of the Republic of Indonesia for sponsoring this research under the Research and Innovation for Advanced Indonesia (RIIM) batch 3 initiative with contract number B-839/II.7.5/FR.06/5/2023

Article Metrics:

Article Info
Section: Review Articles
Language : EN
Recent articles
Analysis Of Ship Main Engine Procurement Process In The Shipyard Industry Using Supply Chain Operation Reference Method (SCOR) (Case Study: PT XYZ) CFD Analysis of Interference Factor in Hydrofoil-Supported Catamarans (HYSUCAT) Analysis of Ambulance Ship Design in Karimunjawa Waters reviewed by Stability and Seakeeping Systematic Considerations for a Ballast Water Treatment System (BWTS) Retrofits: A Review The necessity of implementing AI for enhancing safety in the Indonesian passenger shipping fleet Management Logistics Maritime: A Literature Review More recent articles
Most cited articles
ANALISA GERAKAN SEAKEEPING KAPAL PADA GELOMBANG REGULER ANALISA PENGARUH BENTUK LAMBUNG AXE BOW PADA KAPAL HIGH SPEED CRAFT TERHADAP HAMBATAN TOTAL PENGELASAN PLAT KAPAL MENGGUNAKAN BAHAN BAKAR GAS OPTIMASI BENTUK DEMIHULL KAPAL KATAMARAN UNTUK MENINGKATKAN KUALITAS SEAKEEPING ANALISA TEKNIS KEKUATAN MEKANIS MATERIAL KOMPOSIT BERPENGUAT SERAT AMPAS TEBU (BAGGASE) DITINJAU DARI KEKUATAN TARIK DAN IMPAK More cited articles
  1. T. Ehlers, M. Portier and D. Thoma, "Automation of maritime shipping for more safety and environmental protection," Automatisierungstechnik, vol. 70, no. 5, p. 575–576, May 2022, doi: 10.1515/auto-2022-0003.
  2. K. F. Yuen, X. G. and J. S. L. Lam, "Special issue on ‘Artificial Intelligence & big data in shipping," Maritime Policy & Management, vol. 47, no. 5, p. 575–576, July 2020, doi: 10.1080/03088839.2020.1790052
  3. C. E. Page, "Maximizing maritime safety and environmental protection with AIS: (Automatic identification system)," OCEANS 2017 - Anchorage, 2017
  4. S. N. MacKinnon, R. Weber, F. Olindersson and M. Lundh, "Artificial Intelligence in Maritime Navigation: A Human Factors Perspective," p. 429–435, 2020, doi: 10.1007/978-3-030-50943-9_54
  5. Z. Liu, X. Zhang, Y. Meng and L. Wang, "Numerical calculation of the resistance of catamarans at different distances between two hulls," E3S Web of Conferences, 2021, doi: 10.1051/e3sconf/202128301008
  6. Y. Romadhon and R. V. Vikaliana, "PELAYARAN RAKYAT DALAM PERSPEKTIF SISTEM LOGISTIK NASIONAL," Jurnal Logistik Indonesia, 2018, doi: 10.31334/jli.v1i1.125
  7. D. Faturachman and M. Shariman, "Indonesian’s Ship Safety Assessment Strategy," Computer Science, 2012
  8. UNDANG-UNDANG REPUBLIK INDONESIA NOMOR 17 TAHUN 2008 TENTANG P E L A Y A R A N, Jakarta, 2008
  9. D. Pramono, Budaya Bahari, Gramedia Pustaka Utama, 2005
  10. J. Duha and G. E. S. Saputro, "Blue Economy Indonesia to Increase National Income through the Indian Ocean Rim Association (IORA) in the Order to Empower the World Ocean Rim Association (IORA) in the Order to Empower the World Maritime Axis and Strengthen State Defense," Jurnal Manajemen, Kepemimpinan, dan Supervisi Pendidikan (JMKSP), vol. 7, no. 2, pp. 514-527, 2022, doi: 10.31851/jmksp.v7i2.7915
  11. A. Hebbar, S. Yildiz, N. Kahlouche and J.-U. Schröder-Hinrichs, "Safety of domestic ferries: A scoping study of seven high-risk countries," World Maritime University, Malmö, 2023, doi: 10.21677/rep0123
  12. C. Kontzinos, I. Kanellou, V. Michalakopoulos, S. Mouzakitis, G. Tsapelas, P. Kapsalis, G. Kormpakis and D. Askounis, "STATE-OF-THE-ART ANALYSIS OF ARTIFICIAL INTELLIGENCE APPROACHES IN THE MARITIME INDUSTRY," in Proceedings of the International Conferences on Applied Computing 2022 and WWW/Internet 2022, 2022
  13. Y. Yu and Z. X. Cheng, "The Application of Artificial Intelligence in Ocean Development: In the View of World Expo 2010," Applied Mechanics and Materials, vol. 347–350, p. 2335–2339, 2013, doi: 10.4028/www.scientific.net/AMM.347-350.2335
  14. J. T. Dillingham and A. N. Perakis, "Application of artificial intelligence in the marine industry: problem definition and analysis. Final report. Volume 1. Executive summary. Report for October 1985-February 1987," United States, 1987
  15. Komite Nasional Keselamatan Transportasi, Buku Statistik Investigasi Kecelakaan Transportasi KNKT 2021, Jakarta: Komite Nasional Keselamatan Transportasi, 2021
  16. Komite Nasional Keselamatan Transportasi, Buku Statistik Investigati Kecelakaan Transpsortasi KNKT, Jakarta: Komite Nasional Keselamatan Transportasi, 2022
  17. K. Wróbel, J. Montewkab and P. Kujalac, "Towards the assessment of potential impact of unmanned vessels on maritime transportation safety," Reliability Engineering and System Safety, vol. 165, p. 155–169, 24 March 2017, doi: http://dx.doi.org/10.1016/j.ress.2017.03.029
  18. A. Rawson and M. Brito, "A critique of the use of domain analysis for spatial collision risk assessment," Ocean Engineering, vol. 219, pp. 1-16, 28 10 2020, doi: https://doi.org/10.1016/j.oceaneng.2020.108259
  19. X. Zhou, X. Ruan, H. Wang and G. Zhou, "Exploring spatial patterns and environmental risk factors for global maritime accidents: A 20-year analysis," Ocean Engineering, vol. 286, pp. 1-10, 15 August 2023, doi: https://doi.org/10.1016/j.oceaneng.2023.115628
  20. C. Fan, K. Wrobel, J. Montewka, M. Gil and C. Wan, "A framework to identify factors influencing navigational risk for Maritime Autonomous Surface Ships," Ocean Engineering, vol. 202, pp. 1-15, 7 March 2020, doi: https://doi.org/10.1016/j.oceaneng.2020.107188
  21. W. Qiao, Y. Liu, X. Ma and Y. Liu, "A methodology to evaluate human factors contributed to maritime accident by mapping fuzzy FT into ANN based on HFACS," Ocean Engineering, vol. 197, pp. 1-18, 16 January 2020, doi: https://doi.org/10.1016/j.oceaneng.2019.106892
  22. R. U. Khan, J. Yin, F. S. Mustafa and S. Wang, "Analyzing human factor involvement in sustainable hazardous cargo port operations," Ocean Engineering , vol. 250, pp. 1-11, 14 March 2022, doi: https://doi.org/10.1016/j.oceaneng.2022.111028
  23. T. Cheng, I. B. Utne, B. Wu and Q. Wu, "A novel system-theoretic approach for human-system collaboration safety: Case studies on two degrees of autonomy for autonomous ships," Reliability Engineering and System Safety , vol. 237, pp. 1-16, 13 May 2023, doi: https://doi.org/10.1016/j.ress.2023.109388
  24. M. M. Abaeia, R. Abbassib, V. Garaniyaa, E. Arzaghia and A. B. Toroody, "A dynamic human reliability model for marine and offshore operations in harsh environments," Ocean Engineering, vol. 173, pp. 90-97, 31 December 2018, doi: https://doi.org/10.1016/j.oceaneng.2018.12.032
  25. H. R. Karimi and L. Yanyang, "Guidance and control methodologies for marine vehicles: A survey," Control Engineering Practice, vol. 111, pp. 1-14, 16 March 2021, doi: https://doi.org/10.1016/j.conengprac.2021.104785
  26. Y. Zhou, X. Li and K. F. Yuen, "Holistic risk assessment of container shipping service based on Bayesian Network Modelling," Reliability Engineering and System Safety , vol. 220, pp. 1-16, 31 December 2021, doi: https://doi.org/10.1016/j.ress.2021.108305
  27. S. Islam, F. Goerlandt, X. Feng, M. J. Uddin and Y. Shi, "Improving disasters preparedness and response for coastal communities using AIS ship tracking data," International Journal of Disaster Risk Reduction, vol. 51, pp. 1-13, 18 September 2020, doi: https://doi.org/10.1016/j.ijdrr.2020.101863
  28. W. Shaobo, Z. Yingjun and L. Lianbo, "A collision avoidance decision-making system for autonomous ship based on modified velocity obstacle method," Ocean Engineering, vol. 215, pp. 1-21, 25 August 2020, doi: https://doi.org/10.1016/j.oceaneng.2020.107910
  29. S. Ni, N. Wang, W. Li, Z. Liu, S. Liu, S. Fang and T. Zhang, "A deterministic collision avoidance decision-making system for multi-MASS encounter situation," Ocean Engineering, vol. 266, pp. 1-21, 18 November 2022, doi: https://doi.org/10.1016/j.oceaneng.2022.113087
  30. S. Ni, N. Wang, Z. Qin, X. Yang, Z. Liu and H. Li, "A distributed coordinated path planning algorithm for maritime autonomous surface ship," Ocean Engineering, vol. 271, pp. 1-22, 25 January 2023, doi: https://doi.org/10.1016/j.oceaneng.2023.113759
  31. L. Wang, L. Qing, S. Dong and G. Soares, "Effectiveness assessment of ship navigation safety countermeasures using fuzzy cognitive maps," Safety Science, vol. 117, p. 352–364, 30 April 2019, doi: https://doi.org/10.1016/j.ssci.2019.04.027
  32. L. Zhao, Y. Bai and J. K. Paik, "Achieving optimal-dynamic path planning for unmanned surface vehicles: A rational multi-objective approach and a sensory-vector re-planner," Ocean Engineering, vol. 286, pp. 1-23, 5 August 2023, doi: https://doi.org/10.1016/j.oceaneng.2023.115433
  33. Zhao, Liang, Y. Bai and J. K. Paik, "Global-local hierarchical path planning scheme for unmanned surface vehicles under dynamically unforeseen environments," Ocean Engineering, vol. 280, pp. 1-22, 29 May 2023, https://doi.org/10.1016/j.oceaneng.2023.114750
  34. M. Gao and G.-Y. Shi, "Ship collision avoidance anthropomorphic decision-making for structured learning based on AIS with Seq-CGAN," Ocean Engineering, vol. 217, pp. 1-20, 4 September 2020, doi: https://doi.org/10.1016/j.oceaneng.2020.107922
  35. S. Xie, V. Garofano, X. Chu and R. R. Negenborn, "Model predictive ship collision avoidance based on Q-learning beetle swarm antenna search and neural networks," Ocean Engineering, vol. 193, pp. 1-24, 25 October 2019, doi: https://doi.org/10.1016/j.oceaneng.2019.106609
  36. X. Chen, H. Wu, B. Han, W. Liu, J. Montewka and R. W. Liu, "Orientation-aware ship detection via a rotation feature decoupling supported deep learning approach," Engineering Applications of Artificial Intelligence, vol. 125, pp. 1-16, 24 May 2023, doi: https://doi.org/10.1016/j.engappai.2023.106686
  37. Y. Liu, R. Song, R. Bucknall and X. Zhang, "Intelligent multi-task allocation and planning for multiple unmanned surface vehicles (USVs) using self-organising maps and fast marching method," Information Sciences, vol. 496, 11 May 2019, doi: https://doi.org/10.1016/j.ins.2019.05.029
  38. M. Zhang, P. Kujala, M. Musharraf, J. Zhang and S. Hirdaris, "A machine learning method for the prediction of ship motion trajectories in real operational conditions," Ocean Engineering, vol. 283, pp. 1-24, 13 September 2022, doi: https://doi.org/10.1016/j.oceaneng.2023.114905
  39. Y. Li, M. Liang, H. Li, Z. Yang, L. Du and Z. Chen, "Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping," Engineering Applications of Artificial Intelligence, vol. 126, pp. 1-25, 22 August 2023, doi: https://doi.org/10.1016/j.engappai.2023.107012
  40. C. V. Ribeiro, A. Paes and D. d. Oliveira, "AIS-based maritime anomaly traffic detection: A review," Expert Systems With Applications, vol. 231, pp. 1-18, 1 June 2023, doi: https://doi.org/10.1016/j.eswa.2023.120561
  41. Y. Yang, Z. Shao, Y. Hu, Q. Mei, J. Pan and R. Song, "Geographical spatial analysis and risk prediction based on machine learning for maritime traffic accidents: A case study of Fujian sea area," Ocean Engineering, vol. 266, pp. 1-20, 21 October 2022, doi: https://doi.org/10.1016/j.oceaneng.2022.113106
  42. M. Kanazawa, T. Wang, R. Skulstad, G. Li and H. Zhang, "Knowledge and data in cooperative modeling: Case studies on ship trajectory prediction," Ocean Engineering, vol. 266, pp. 1-13, 8 November 2022, doi: https://doi.org/10.1016/j.oceaneng.2022.112998
  43. Z. Yin, D. Yang and X. Bai, "Vessel destination prediction: A stacking approach," Transportation Research, vol. Part C 145, pp. 1-16, 15 November 2022, doi: https://doi.org/10.1016/j.trc.2022.103951
  44. P. G. Siqueira, M. d. C. Moura and H. O. Duarte, "A Bayesian population variability based method for estimating frequency of maritime accidents," Process Safety and Environmental Protection, vol. 163, p. 308–320, 2022, doi: https://doi.org/10.1016/j.psep.2022.05.035
  45. M. Zhang, D. Zhang, S. Fu, P. Kujala and S. Hirdaris, "A predictive analytics method for maritime traffic flow complexity estimation in inland waterways," Reliability Engineering and System Safety, vol. 220, pp. 1-18, 2 January 2022, doi: https://doi.org/10.1016/j.ress.2021.108317
  46. H. H. Dreany and R. Roncaceb, "A cognitive architecture safety design for safety critical systems," Reliability Engineering and System Safety, vol. 191, pp. 1-14, 28 June 2019, doi: https://doi.org/10.1016/j.ress.2019.106555
  47. T. Vairo, D. Cademartori, D. Clematis, M. P. Carpanese and B. Fabiano, "Solid oxide fuel cells for shipping: A machine learning model for early detection of hazardous system deviations," Process Safety and Environmental Protection, vol. 172, p. 184–194, 11 February 2023, doi: https://doi.org/10.1016/j.psep.2023.02.022
  48. Ö. F. Görçün, D. Pamucar, R. Krishankumar and H. Küçükönder, "The selection of appropriate Ro-Ro Vessel in the second-hand market using the WASPAS’ Bonferroni approach in type 2 neutrosophic fuzzy environment," Engineering Applications of Artificial Intelligence, vol. 117, pp. 1-27, 30 October 2022, doi: https://doi.org/10.1016/j.engappai.2022.105531
  49. A. L. Díaz-Secades, R. Gonzalez, N. Rivera, E. Montanes and J. R. Quevedo, "Waste heat recovery system for marine engines optimized through a preference learning rank function embedded into a Bayesian optimizer," Ocean Engineering, vol. 281, pp. 1-19, 22 May 2023, doi: https://doi.org/10.1016/j.oceaneng.2023.114747
  50. I. d. l. P. Zarzuelo, M. J. F. Soeane and B. L. Bermúdez, "Industry 4.0 in the port and maritime industry: A literature review," Journal of Industrial Information Integration, vol. 20, pp. 1-18, 02 October 2020, doi: https://doi.org/10.1016/j.jii.2020.100173
  51. S. Filom, A. M. Amiri and S. Razavi, "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research, vol. Part E 161, pp. 1-30, 28 April 2022, doi: https://doi.org/10.1016/j.tre.2022.102722
  52. R. Miętkiewicz, "LNG supplies’ security with autonomous maritime systems at terminals’ areas," Safety Science, vol. 142, pp. 1-15, 12 July 2021, doi: https://doi.org/10.1016/j.ssci.2021.105397
  53. R. U. Khan, J. Yin, F. S. Mustafa and N. Anning, "Risk assessment for berthing of hazardous cargo vessels using Bayesian networks," Ocean and Coastal Management, vol. 210, pp. 1-11, 3 June 2021, doi: https://doi.org/10.1016/j.ocecoaman.2021.105673

Last update:

No citation recorded.

Last update: 2024-12-25 22:00:04

No citation recorded.