PENGARUH MANAJEMEN PERAWATAN PREVENTIF TERHADAP TINGKAT KEANDALAN DAN KESELAMATAN MESIN KAPAL

Kresno Yuntoro; Kundori Kundori; Ariana Oktavia; Asep Hendiawan; Sugeng Hariyadi

doi:10.59003/nhj.v6i2.2232

Authors

Kresno Yuntoro Politeknik Ilmu Pelayaran Semarang
Kundori Kundori Universitas Maritim AMNI
Ariana Oktavia Universitas Maritim AMNI
Asep Hendiawan AKMI Suaka Bahari
Sugeng Hariyadi Universitas Maritim AMNI

DOI:

https://doi.org/10.59003/nhj.v6i2.2232

Keywords:

Safety, Maintenance Management, Marine Engines, Shipping Companies

Abstract

This study aims to analyze the effect of preventive maintenance management on the reliability and safety of marine engines. This study uses a quantitative approach with an associative-causal research design, which aims to measure and analyze cause-and-effect relationships. Data analysis in this study was carried out in stages using SPSS and AMOS software. The results show that preventive maintenance management has a positive and significant effect on ship engine reliability (β = 0.724; t = 11.947; p < 0.001) and operational safety (β = 0.689; t = 10.541; p < 0.001). Strengthening the spare parts management system and developing the technical competence of engine crews need to be given special priority in the agenda for improving the quality of PMS in national shipping companies. This study provides empirical evidence that consistent implementation of preventive maintenance management, particularly through strengthening the PMS system, is not merely a matter of fulfilling regulatory aspects, but an operational strategy that is scientifically proven to be able to increase fleet reliability while reducing shipping safety risks.

Downloads

Download data is not yet available.

References

Alfionita, S., & Alifin, F. I. (2023). Preventive maintenance analysis based on mean time between failure (MTBF) and mean time to repair (MTTR). Angkasa: Jurnal Ilmiah Bidang Teknologi, 15(2), 201-214. http://dx.doi.org/10.28989/angkasa.v15i2.1833

Allianz, 2026. Safety and Shipping Review 2026. https://commercial.allianz.com/news-and-insights/reports/shipping-safety.html

Budimir, D., Grgurević, I., & Leder Horina, J. (2025). Reliability Methods in Maintenance and Failure Analysis of Marine Systems. Promet-Traffic&Transportation, 37(3), 570-584. https://doi.org/10.7307/ptt.v37i3.1065

Cicek, K. (2026). Marine engineering systems failure analysis using extended failure modes and effects analysis with quality function deployment: maritime house of reliability. International Journal of Quality & Reliability Management, 43(2), 516-538. https://doi.org/10.1108/IJQRM-05-2025-0157

Daya, A. A., & Lazakis, I. (2024). Systems reliability and data driven analysis for marine machinery maintenance planning and decision making. Machines, 12(5), 294. https://doi.org/10.3390/machines12050294

Farrar, T., Blignaut, R., Luus, R., & Steel, S. (2025). A review and comparison of methods of testing for heteroskedasticity in the linear regression model. Journal of Applied Statistics, 52(16), 3121-3150. https://doi.org/10.1080/02664763.2025.2575038

Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Does PLS have advantages for small sample size or non-normal data?. MIS quarterly, 36(3), 981-1001. https://doi.org/10.2307/41703490

Hamza, Z., & Hacene, S. (2019). Reliability and safety analysis using fault tree and Bayesian networks. International Journal of Computer Aided Engineering and Technology, 11(1), 73-86. https://doi.org/10.1504/IJCAET.2019.096720

Hsu, Y. C. (2023). Assessment of criteria of ship classification societies. Maritime Policy & Management, 50(7), 980-1004. https://doi.org/10.1080/03088839.2022.2061061

Jimenez, V. J., Bouhmala, N., & Gausdal, A. H. (2020). Developing a predictive maintenance model for vessel machinery. Journal of Ocean Engineering and Science, 5(4), 358-386. https://doi.org/10.1016/j.joes.2020.03.003

Kalafatelis, A. S., Nomikos, N., Giannopoulos, A., Alexandridis, G., Karditsa, A., & Trakadas, P. (2025). Towards predictive maintenance in the maritime industry: A component-based overview. Journal of Marine Science and Engineering, 13(3), 425. https://doi.org/10.3390/jmse13030425

Karatuğ, Ç., Arslanoğlu, Y., & Soares, C. G. (2023). Review of maintenance strategies for ship machinery systems. Journal of Marine Engineering & Technology, 22(5), 233-247. https://doi.org/10.1080/20464177.2023.2180831

Kartika, I. M., Sumada, I. M., Suwandana, I. M. A., Adnyana, Y., Sedana, I. D. G. P., & Meryawan, I. W. (2024). Assessing the performance and competitive strategies of bamboo weaving MSMEs in Bali using quantitative associative causal design. Journal of Applied Data Sciences, 5(3), 1189-1209. https://doi.org/10.47738/jads.v5i3.320

Khan, F. I., & Haddara, M. M. (2003). Risk-based maintenance (RBM): a quantitative approach for maintenance/inspection scheduling and planning. Journal of loss prevention in the process industries, 16(6), 561-573. https://doi.org/10.1016/j.jlp.2003.08.011

Lazakis, I., Raptodimos, Y., & Varelas, T. (2018). Predicting ship machinery system condition through analytical reliability tools and artificial neural networks. Ocean Engineering, 152, 404-415. 10.1016/j.oceaneng.2017.11.017

Lim, W. M. (2025). What is quantitative research? An overview and guidelines. Australasian Marketing Journal, 33(3), 325-348. https://doi.org/10.1177/14413582241264622

Mujiarto, I., Asmoro, E. I., & Kundori, K. (2022). Pengukuran laju kerusakan dengan mengindikasikan nilai mtbf dalam manajemen perawatan mesin pada PT. AIC. Jurnal Teknik Mesin, Industri, Elektro Dan Informatika, 1(3), 14-23. https://doi.org/10.55606/jtmei.v1i3.491

Musril, H. A., Saludin, S., Firdaus, W., Usanto, S., Kundori, K., & Rahim, R. (2023). Using k-NN artificial intelligence for predictive maintenance in facility management. International Journal of Electrical and Electronics Engineering, 10(6), 1-8. https://doi.org/10.14445/23488379/IJEEE-V10I6P101

Prayogo, D., Arifin, M., Runadi, T., Presetiawan, A., Rahmana, H. W., & Pratama, R. A. (2025). Analysis of the Influence of Skills Training on Ships Leadership with an Understanding of the International Safety Management (ISM) Code as a Mediation Variable. TEM Journal, 14(2). https://doi.org/10.18421/TEM142-34

Simion, D., Postolache, F., Fleacă, B., & Fleacă, E. (2024). Ai-driven predictive maintenance in modern maritime transport—Enhancing operational efficiency and reliability. Applied Sciences, 14(20), 9439. https://doi.org/10.3390/app14209439