Identification of the Needs of Shipmasters and Shipping Stakeholders Towards Ship Safety Score for Shipping Safety Based on Logistic Regression Model

Authors

  • Eko Prasetyo Doctoral Student in Defence Science, Indonesia Defence University
  • Siswo Hadi Sumantri Doctoral Student in Defence Science, Indonesia Defence University
  • Syaiful Anwar Doctoral Programme in Defence Science, Indonesia Defence University
  • Pujo Widodo Doctoral Programme in Defence Science, Indonesia Defence University

Keywords:

shipping safety, safety score, logistics regression, weather

Abstract

An essential component of Indonesia's marine industry is shipping safety. This research aims to determine how important a vessel's safety score is as a potential indicator for reducing the probability of maritime accidents. To determine the meteorological variables that significantly impact ship accidents, we created a logistic regression model using survey data obtained from skippers of vessels operating within Indonesia. Furthermore, the study's findings demonstrate the critical need for objective assessment standards to raise shipping safety. This study offers important new information on creating and implementing thorough safety score criteria for Indonesian sailing license issuance.

Downloads

Download data is not yet available.

References

Aalberg, A. L., Bye, R. J., & Ellevseth, P. R. (2022). Risk factors and navigation accidents: A historical analysis comparing accident-free and accident-prone vessels using indicators from AIS data and vessel databases. Maritime Transport Research, 3, 100062.

Devore, Jay L. (2011). Probability and Statistics for Engineering and the Sciences (8th ed.). Boston, MA: Cengage Learning. pp. 508–510. ISBN 978-0-538-73352-6.

Laporan investigasi kecelakaan kapal (http://knkt.go.id/). Diakses pada 6 Desember 2023.

Menteri Perhubungan. (2018, 2 Maret). Empat puluh persen jalur perdagangan dunia melewati Indonesia. Kementerian Perhubungan Republik Indonesia. https://dephub.go.id/post/read/empat-puluh-persen-jalur-perdagangan-dunia-melewati-indonesia

Permenhub Nomor PM 28 Tahun 2022 Pasal 11 ayat (8).

Prilana, R. E., BOWO, L. P., & FURUSHO, M. (2020). Maritime Cargo Accidents in Indonesia for the period 2013-2018. Navigation, 211, 19-20.

Rezaee, Sara, Pelot, Ronald, Ghasemi, Alireza, The effect of extreme weather conditions on commercial fishing activities and vessel incidents in Atlantic Canada, Ocean & Coastal Management, Volume 130, 2016, Pages 115-127, ISSN 0964-5691, https://doi.org/10.1016/j.ocecoaman.2016.05.011.

Toffoli, A., Lefevre, J. M., Bitner-Gregersen, E., & Monbaliu, J. (2005). Towards the identification of warning criteria: analysis of a ship accident database. Applied Ocean Research, 27(6), 281-291.

Undang Undang No. 17 Tahun 2008 tentang Pelayaran Pasal 219.

Wang, J., Yang, Z., & Liu, J. (2014). Bayesian network with quantitative input for maritime risk analysis.

Wu, Y., Pelot, R. P., & Hilliard, C. (2009). The Influence of Weather Conditions on the Relative Incident Rate of Fishing Vessels. Risk Analysis, 29(7), 985-999. https://doi.org/10.1111/j.1539-6924.2009.01217.x

Xu, T., Liu, X., & Hu, S. (2020). Maritime accidents in New Zealand from 2015 to 2018: revealing recommendations from statistical review. Journal of the Royal Society of New Zealand, 50(4), 509-522.

Zhang, L., Yan, X., & Yang, Z. (2021). Application of Cloud Model and Bayesian Network to Piracy Risk Assessment.

Zhang, Y., Teixeira, A.P., & Guedes Soares, C. (2021). The Development of a Bayesian Network Framework with Model Validation for Maritime Accident Risk Factor Assessment.

Zhang, Y., Yan, X., & Yang, Z. (2020). A machine learning approach for monitoring ship safety in extreme conditions. Journal of Shipping and Trade, 5(1), 1-15.

Downloads

Published

2024-02-16

How to Cite

Prasetyo, E. ., Sumantri, S. H. ., Anwar, S. ., & Widodo, P. . (2024). Identification of the Needs of Shipmasters and Shipping Stakeholders Towards Ship Safety Score for Shipping Safety Based on Logistic Regression Model. JUKI : Jurnal Komputer Dan Informatika, 5(2), 370–377. Retrieved from https://ioinformatic.org/index.php/JUKI/article/view/445