ABSTRACT Piracy and armed robbery have become serious problems in maritime economic activities, particularly in the Straits of Malacca and Singapore. These crimes occur due to various influential factors, necessitating analysis through incident reports. This study aims to identify the factors influencing piracy and armed robbery in these areas using natural language processing (NLP) technologies. Data were obtained from reports compiled by relevant organizations such as the Global Integrated Shipping Information System (GISIS) from the International Maritime Organization (IMO) and Annual Reports on Piracy and Armed Robbery from the Regional Cooperation Agreement on Combating Piracy and Armed Robbery against Ships in Asia (ReCAAP). These data were reconstructed through NLP and further analyzed using the Bidirectional Encoder Representations from Transformer (BERT) model derivative, BERTopic. Several topics were obtained from the analysis, suggesting key factors influencing the outcome of piracy and armed robbery incidents. These topics will serve as the basis for suggestions and solution provisions that can be effectively applied and utilized to further decrease the frequency and success rate of piracy and armed robbery in the future. The findings provide crucial insights for developing strategies to mitigate the impact of these criminal activities on maritime economic activities.
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