Enhancing Maritime Safety Standards through Advanced Technologies and Professional Education

Retno Sawitri  Wulandari; Larsen Barasa; Marihot Simanjuntak; Mudakir; Rosna Yuherlina Siahaan

doi:10.38204/tematik.v11i1.1883

Retno Sawitri Wulandari Sekolah Tinggi Ilmu Pelayaran - Jakarta
Larsen Barasa Maritime Institute (Sekolah Tinggi Ilmu Pelayaran) Jakarta
Marihot Simanjuntak Maritime Institute (Sekolah Tinggi Ilmu Pelayaran)
Mudakir Maritime Institute (Sekolah Tinggi Ilmu Pelayaran) Jakarta
Rosna Yuherlina Siahaan Maritime Institute (Sekolah Tinggi Ilmu Pelayaran) Jakarta

DOI: https://doi.org/10.38204/tematik.v11i1.1883

Keywords: maritime safety, advanced technologies, machine learning, maritime education, safety standards

Abstract

This research explores the integration of advanced technologies, such as machine learning and sensor data analysis, into maritime education to enhance safety standards. The study focuses on cadets' knowledge, attitudes, and perceptions of safety practices and examines incident data to predict and prevent maritime incidents. Findings reveal that while cadets have substantial knowledge of safety regulations, their attitudes towards safety culture need reinforcement. The alignment of education programs with industry requirements and adherence to international standards are emphasized. Additionally, the integration of technology into curricula is crucial for preparing future maritime professionals. The research underscores the need for a comprehensive approach combining technical knowledge, professional attitudes, and advanced technology to significantly improve safety standards and ensure the sustainability of the maritime industry.

Downloads

Download data is not yet available.

References

V. A. Tsevtkov, K. K. Zoidov, and A. A. Medkov, “The implementation of transportation and transit projects on the basis of public-private partnership in Russia,” Ekon. Reg. Econ. Reg., no. 4, pp. 977–988, 2016.

H. Sasana, “Fiscal decentralization and regional economic growth,” Econ. Dev. Anal. J., vol. 8, no. 1, pp. 108–119, 2019.

M. Hänninen, O. A. V. Banda, and P. Kujala, “Bayesian network model of maritime safety management,” Expert Syst. Appl., vol. 41, no. 17, pp. 7837–7846, 2014.

Z. H. Munim, M. Dushenko, V. J. Jimenez, M. H. Shakil, and M. Imset, “Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions,” Marit. Policy Manag., vol. 47, no. 5, pp. 577–597, 2020.

O. Erdogan and E. Demirel, “New Technologies in Maritime Education and Training, Turkish Experiment.,” Univers. J. Educ. Res., vol. 5, no. 6, pp. 947–952, 2017.

M. E. Rahayuningsih and E. Winarni, “The Influence of Logistic English Proficiency Towards Working Ability of Maritime University Alumni,” CaLLs (Journal Cult. Arts, Lit. Linguist., vol. 7, no. 2, pp. 225–246, 2021.

A. Mandaraka-Sheppard, Modern maritime law and risk management. CRC Press, 2014.

K. Koyuncu and L. Tavacıoğlu, “Usage of Maritime Sign Language on Board,” Eng. Sci., vol. 14, no. 4, pp. 183–199, 2019.

C. M. Smith and D. Shaw, “The characteristics of problem structuring methods: A literature review,” Eur. J. Oper. Res., vol. 274, no. 2, pp. 403–416, 2019.

A. Giovanni, L. D. Fathimahhayati, and T. A. Pawitra, “Risk Analysis of Occupational Health and Safety Using Hazard Identification, Risk Assessment and Risk Control (HIRARC) Method (Case Study in PT Barokah Galangan Perkasa),” IJIEM (Indonesian J. Ind. Eng. Manag., vol. 4, pp. 198–211, 2023.

J. C. Richards and W. A. Renandya, Methodology in language teaching: An anthology of current practice. Cambridge university press, 2002.

Y. Tong, Kinshuk, and X. Wei, “Teaching design and practice of a project-based blended learning model,” Int. J. Mob. Blended Learn., vol. 12, no. 1, pp. 33–50, Jan. 2020, doi: 10.4018/IJMBL.2020010103.

H. Kim, J. S. Sefcik, and C. Bradway, “Characteristics of qualitative descriptive studies: A systematic review,” Res. Nurs. Health, vol. 40, no. 1, pp. 23–42, 2017.

J. M. Knies, “A qualitative study of college cadet women’s leadership identity development in a military training environment.” Virginia Tech, 2019.

B. N. Yusuf, “Are we prepared enough? A case study of challenges in online learning in a private higher learning institution during the COVID-19 outbreaks,” Adv. Soc. Sci. Res. J., vol. 7, no. 5, pp. 205–212, May 2020, doi: 10.14738/assrj.75.8211.

F. Chen, P. Deng, J. Wan, D. Zhang, A. V Vasilakos, and X. Rong, “Data mining for the internet of things: literature review and challenges,” Int. J. Distrib. Sens. Networks, vol. 11, no. 8, p. 431047, 2015.

D. Dalaklis, “Safety and security in shipping operations,” Shipp. Oper. Manag., pp. 197–213, 2017.

S. L. C. da Silva and F. G. Amaral, “Critical factors of success and barriers to the implementation of occupational health and safety management systems: A systematic review of literature,” Saf. Sci., vol. 117, pp. 123–132, 2019.

Y. Guangpeng, H. Yin, and L. Fengze, “Computer vision technology based on image optical processing in visual packaging art design simulation,” Opt. Quantum Electron., vol. 56, no. 4, p. 529, 2024, doi: 10.1007/s11082-023-06142-4.

N. Pradhan, A. S. Singh, and A. Singh, “Cognitive computing: Architecture, technologies and intelligent applications,” Mach. Learn. Cogn. Comput. Mob. Commun. Wirel. networks, pp. 25–50, 2020.

A. Ullah et al., “Smart cities: The role of Internet of Things and machine learning in realizing a data-centric smart environment,” Complex Intell. Syst., vol. 10, no. 1, pp. 1607–1637, 2024.