Traffic accident classification using IndoBERT

Muhammad Alwan Naufal, Abba Suganda Girsang


Traffic accidents are a widespread concern globally, causing loss of life, injuries, and economic burdens. Efficiently classifying accident types is crucial for effective accident management and prevention. This study proposes a practical approach for traffic accident classification using IndoBERT, a language model specifically trained for Indonesian. The classification task involves sorting accidents into four classes: car accidents, motorcycle accidents, bus accidents, and others. The proposed model achieves a 94% accuracy in categorizing these accidents. To assess its performance, we compared IndoBERT with traditional methods, random forest (RF) and support vector machine (SVM), which achieved accuracy scores of 85% and 87%, respectively. The IndoBERT-based model demonstrates its effectiveness in handling the complexities of the Indonesian language, providing a useful tool for traffic accident classification and contributing to improved accident management and prevention strategies.


Classification; IndoBERT; Machine learning; Traffic accident; Social media

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The International Journal of Informatics and Communication Technology (IJ-ICT)
p-ISSN 2252-8776, e-ISSNĀ 2722-2616
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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