Real-Time Web-Based Ship Collision Risk Detection Using AIS Data and Collision Risk Index (CRI)

Authors

  • I Made Dwi Putra Asana Doctor of Engineering Science Department, Udayana University, Indonesia
  • I Made Oka Widyantara Electrical Engineering Department, Udayana University, Indonesia
  • Linawati Electrical Engineering Department, Udayana University, Indonesia
  • Dewa Made Wiharta Electrical Engineering Department, Udayana University, Indonesia
  • I Gusti Ngurah Satya Wikananda Informatics Department, Institute Business and Technology Indonesia, Indonesia

DOI:

10.33395/sinkron.v9i4.15106

Keywords:

AIS, Collision Risk Index, DBSCAN, ship monitoring, maritime safety, web-based application

Abstract

The high density of maritime traffic in Indonesian waters, particularly in the Lombok Strait and Nusa Penida region, increases the risk of ship collisions, especially among vessels lacking adequate navigation systems. This study presents the development of a web-based system for real-time ship monitoring and collision risk assessment using Automatic Identification System (AIS) data. The system integrates a backend powered by FastAPI and MongoDB with a frontend built using React JS. AIS data is collected from a base station and processed to detect ship encounters using the DBSCAN clustering algorithm combined with Haversine distance to identify encounter detection. The risk assessment applies the Collision Risk Index (CRI) method by calculating DCPA (Distance to Closest Point of Approach) and TCPA (Time to Closest Point of Approach), allowing for graded risk categorization. Real-time risk notifications are delivered via WebSocket, and the interface includes interactive maps, ship detail views, and maritime weather information from the BMKG API. The system achieved high responsiveness, with an average detection time of 0.0075 seconds per ship and an end-to-end response time of approximately 61 milliseconds. Functional and usability tests show that the system effectively supports early detection of collision risks and improves maritime situational awareness. The proposed solution is scalable and applicable for maritime safety monitoring in busy sea routes, contributing to safer navigation and proactive decision-making.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Bachtiar, A., Trilia, D., Hia, H. A. F., Zafirawan, R. A., & Supriyadi, A. A. (2024). Pengawasan Maritim Efektif Melalui Implementasi Automatic Identification System (Ais) Untuk Jalur Pelayaran Surabaya-Makassar. Majalah Ilmiah Globe, 26(2), 57–66. https://doi.org/10.24895/gl.2024.26.2.57-66

Chen, D., Dai, C., Wan, X., & Mou, J. (2015). A research on AIS-based embedded system for ship collision avoidance. 2015 International Conference on Transportation Information and Safety (ICTIS), 512–517. https://doi.org/10.1109/ICTIS.2015.7232141

Darilaut.id. (2022). 2018-2021, Sebanyak 483 Kecelakaan Kapal Perikanan Indonesia. https://darilaut.id/berita/2018-2021-sebanyak-483-kecelakaan-kapal-perikanan-indonesia

Enda, D., Agustiawan, A., Milchan, M., & Pratiwi, E. (2021). Rancang Bangun Aplikasi AIS Backend Untuk Pemantauan Lalu Lintas Kapal di Selat Melaka. INOVTEK Polbeng - Seri Informatika, 6(2), 284. https://doi.org/10.35314/isi.v6i2.2139

Hassel, M., Aalberg, A. L., & Nordkvist, H. A. (2019). An Advanced Method for Detecting Exceptional Vessel Encounters in Open Waters from High Resolution AIS Data. Proceedings of the 29th European Safety and Reliability Conference (ESREL), June, 1709–1714. https://doi.org/10.3850/978-981-11-2724-3_0038-cd

Herianto Herianto, Fajri Profesio Putra, & Muhammad Asep Subandri. (2024). Peningkatan Akurasi Pada Sistem Monitoring Posisi Kapal Menggunakan Metode Kalman Filter. Saturnus : Jurnal Teknologi Dan Sistem Informasi, 2(4), 249–260. https://doi.org/10.61132/saturnus.v2i4.354

Idris, M. A., Apriyanto, A., & Rahmawati. (2023). Pemetaan Produksi Perikanan Tangkap di Indonesia dengan Menggunakan Metode DBSCAN. Journal of Mathematics: Theory and Applications, 5(2), 80–86. https://doi.org/10.31605/jomta.v5i2.2930

Li, W., Zhong, L., Liu, Y., & Shi, G. (2023). Ship Intrusion Collision Risk Model Based on a Dynamic Elliptical Domain. Journal of Marine Science and Engineering, 11(6), 1122. https://doi.org/10.3390/jmse11061122

Li, W., Zhong, L., Xu, Y., & Shi, G. (2022). Collision Risk Index Calculation Based on an Improved Ship Domain Model. Journal of Marine Science and Engineering, 10(12), 2016. https://doi.org/10.3390/jmse10122016

Lisowski, J. (2001). Determining the optimal ship trajectory in a collision situation. Proceedings of the IX International Scientific and Technical Conference on Marine Traffic Engineering, Szczecin, Poland, January, 192–201.

Liu, H., Deng, R., & Zhang, L. (2016). The application research for ship collision avoidance with hybrid optimization algorithm. 2016 IEEE International Conference on Information and Automation (ICIA), 760–767. https://doi.org/10.1109/ICInfA.2016.7831921

Nguyen, M., Zhang, S., & Wang, X. (2018). A Novel Method for Risk Assessment and Simulation of Collision Avoidance for Vessels based on AIS. Algorithms, 11(12), 204. https://doi.org/10.3390/a11120204

Pratomo, A. H., Kaswidjanti, W., & Mu’arifah, S. (2020). Implementasi Algoritma Region of Interest (Roi) Untuk Meningkatkan Performa Algoritma Deteksi Dan Klasifikasi Kendaraan Implementation of Region of Interest (Roi) Algorithm To Improve Car Detection and Classification Algorithm. Jurnal Teknologi Informasi Dan Ilmu Komputer (JTIIK), 7(1), 155–162. https://doi.org/10.25126/jtiik.202071718

Setiyantara, Y., Ningrum Astriawati, Pertiwi, Y., Ade Chandra Kusuma, & Thomas Wahyu Bagaskoro. (2023). Optimalisasi Pengoperasian AIS (Automatic Identification System) Dalam Upaya Menjaga Keselamatan Pelayaran. Meteor STIP Marunda, 16(1), 1–6. https://doi.org/10.36101/msm.v16i1.268

Tedyyana, A., Ratnawati, F., & Danuri, D. (2023). Platform Monitoring Berbasis Web untuk Sistem Stabilitas dan Pelacakan Kapal Nelayan. Digital Zone: Jurnal Teknologi Informasi Dan Komunikasi, 14(2), 168–178. https://doi.org/10.31849/digitalzone.v14i2.16419

van Iperen, E. (2015). Classifying Ship Encounters to Monitor Traffic Safety on the North Sea from AIS Data. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, 9(1), 51–58. https://doi.org/10.12716/1001.09.01.06

Wright, D., Janzen, C., Bochenek, R., Austin, J., & Page, E. (2019). Marine Observing Applications Using AIS: Automatic Identification System. Frontiers in Marine Science, 6. https://doi.org/10.3389/fmars.2019.00537

Wulandari, A. S., Saepudin, A., Kinanti, M. P., Sudesi, Z., Saifudin, A., & Yulianti, Y. (2022). Pengujian Aplikasi Sistem Informasi Akademik Berbasis Web Menggunakan Metode Black Box Testing Equivalence Partitioning. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 5(2), 102. https://doi.org/10.32493/jtsi.v5i2.17561

Zaccone, R. (2021). COLREG-Compliant Optimal Path Planning for Real-Time Guidance and Control of Autonomous Ships. Journal of Marine Science and Engineering, 9(4), 405. https://doi.org/10.3390/jmse9040405

Zhang, Y., & Li, W. (2022). Dynamic Maritime Traffic Pattern Recognition with Online Cleaning, Compression, Partition, and Clustering of AIS Data. Sensors, 22(16), 6307. https://doi.org/10.3390/s22166307

Downloads


Crossmark Updates

How to Cite

Asana, I. M. D. P., Widyantara, I. M. O. ., Linawati, L., Wiharta, D. M. ., & Wikananda, I. G. N. S. . (2025). Real-Time Web-Based Ship Collision Risk Detection Using AIS Data and Collision Risk Index (CRI). Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(4), 1942-1952. https://doi.org/10.33395/sinkron.v9i4.15106

Issue

Vol. 9 No. 4 (2025): Articles Research October 2025

Section

Articles

License

Copyright (c) 2025 I Made Dwi Putra Asana, I Made Oka Widyantara, Linawati, Dewa Made Wiharta, I Gusti Ngurah Satya Wikananda

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Crossref
Scopus
Google Scholar
Europe PMC

Most read articles by the same author(s)