Detecting Anomalous Ship Movements in Indonesian Seas Using Convolutional Neural Networks

Baharuddin, Fikri and Prasetyo, Daniel Hary and Prasetyo, Vincentius Riandaru (2026) Detecting Anomalous Ship Movements in Indonesian Seas Using Convolutional Neural Networks. In: The 2nd International Conference on Applied Sciences and Smart Technologies (InCASST 2025), 15 Oktober 2025, Yogyakarta.

PDF (Published Conference Paper - InCASST 2025) e3sconf_incasst2026_02013.pdf - Published Version Download (796kB)

Abstract

Detecting unusual ship movements is a crucial feature of maritime surveillance, particularly in Indonesian waters, where illegal fishing, unauthorized resource exploitation, drifting ships, and unauthorized navigation pose significant threats to safety and security. This research proposes a Convolutional Neural Network (CNN)-based methodology for categorizing ship movement behaviors into two classifications: drifting and non-drifting. The dataset has 79,200 image-based samples, uniformly divided between the two categories. The proposed model is trained and tested using accuracy, recall, precision, F-score performance metrics. The experiment shows that the resulting model successfully classifies the movement of the ship well. This is evidenced by a testing accuracy of 0.98, a precision of 99%, a recall of 95%, an F-score of 97%, indicating that the CNN was highly accurate and robust, suggesting it could be utilized in realtime maritime anomaly detection systems.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Engineering > Department of Informatic
Depositing User:	FIKRI BAHARUDDIN
Date Deposited:	10 Mar 2026 03:19
Last Modified:	10 Mar 2026 03:19
URI:	http://repository.ubaya.ac.id/id/eprint/50416

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