Abstract

BPS Statistics utilizes big data to improve the accuracy and speed of statistical data. One popular form of big data is the Automatic Identification System (AIS), which is used to monitor and communicate ship movements. This research aims to to build algorithm to monitor shipping activities in Indonesian waters, including incoming and outgoing Indonesian waters, duration at port, and in port, and overseas visits. The implementation using the Distance-Based and Cluster-Based Area of Interest (AOI) approach, as well as Apache Spark for big data processing. This research also developed a Standard Operating Procedures (SOPs) for AIS data preprocessing, including validation, detection and handling of missing or inconsistent data, and correction of manual error. One of the steps is to ensure the Maritime Mobile Service Identity (MMSI) format is correct. Thus, the accuracy and reliability of AIS data as official statistics is improved, providing added value in operations, market predictions, and economic impact analysis of the maritime sector in Indonesia.