Automatic Identification System (AIS) Data Reliability and Its Implications for Maritime Safety in Indonesia

Abstract

The Automatic Identification System (AIS) is central to vessel monitoring, traffic management, and maritime safety, yet concerns remain regarding its reliability due to incomplete, inaccurate, or delayed reporting. This study assesses AIS data from the Indonesian maritime domain, focusing on four parameters: completeness, accuracy, consistency, and timeliness. AIS records data were preprocessed through data cleaning, filtering, and detection of missing values in static fields such as draught, beam, LOA, deadweight, and gross tonnage (GT). Statistical and spatial-temporal analyses using Python were applied to quantify missing data, identify anomalies, and evaluate reporting intervals. Results show high completeness (97.5%), although missing draught data (6.77%) limited under-keel clearance assessments, while small gaps in beam and LOA affected collision risk modeling and berth allocation. Accuracy was moderate, with invalid speed and course records observed, whereas consistency was excellent, with MMSI and ship names fully aligned. Timeliness proved weakest, with median reporting intervals (8,380 seconds) exceeding IMO standards, restricting real-time navigational use but remaining suitable for long-term monitoring. Overall, AIS in Indonesia is reliable for strategic traffic analysis but insufficient for operational safety management. Strengthening reporting compliance, integrating port and registry databases, and applying anomaly detection and satellite AIS are recommended to enhance maritime safety.