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Media type:
E-Article
Title:
Traffic accident classification using IndoBERT
Contributor:
Naufal, Muhammad Alwan;
Girsang, Abba Suganda
Published:
Institute of Advanced Engineering and Science, 2024
Published in:
International Journal of Informatics and Communication Technology (IJ-ICT), 13 (2024) 1, Seite 42
Language:
Not determined
DOI:
10.11591/ijict.v13i1.pp42-49
ISSN:
2722-2616;
2252-8776
Origination:
Footnote:
Description:
<span>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.</span>