Implementation of BERTopic for Topic Modelling on KNKT’s Aviation Accident Investigation Reports

Abstract

Aircraft accident investigation reports contain important information regarding the chronology of events, findings, contributing factors, and safety recommendations that can be used to understand accident patterns. However, this information is generally presented in the form of unstructured text, making manual analysis less efficient. This research aims to apply BERTopic to identify latent themes in aviation accident investigation reports published by the National Transportation Safety Committee (KNKT). A total of 182 investigation reports classified as Final Reports were processed through text extraction, corpus formation, and preprocessing, resulting in 172 documents used for topic modelling. BERTopic was implemented using sentence embedding, UMAP dimensionality reduction, HDBSCAN clustering, and c-TF-IDF-based topic representation. The modelling results yielded ten main topics reflecting various aspects of aviation safety, including technical, operational, and human factors. Evaluation showed that BERTopic achieved a topic coherence (Cv) value of 0.6111 and generated more specific keywords compared to Latent Dirichlet Allocation (LDA). The research results indicate that BERTopic is capable of effectively extracting latent themes from KNKT investigation reports and has the potential to support the analysis of aviation accident patterns and serve as a foundation for the development of domain knowledge-based research in the field of aviation safety.