<![CDATA[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.]]>
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