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Ega Muhammad Atsir
Duta Bangsa University, Indonesia
Computer Science & IT

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Traffic Accident Severity Classification System Using Random Forest Algorithm Ega Muhammad Atsir; Nurmalitasari Nurmalitasari; Aprilisa Arum Sari
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i02.2089

Abstract

Traffic accidents pose a major concern in many countries, including Indonesia, causing considerable losses, injuries, and fatalities each year. Properly classifying the severity of these incidents is essential for authorities to establish preventive actions, apply effective countermeasures, and improve overall road safety. Conventional statistical techniques often fall short in capturing the intricate relationships among multiple influencing variables, such as weather, driver experience, vehicle type, number of vehicles, and casualty figures. To address this limitation, this study proposes a machine learning–based classification method using the Random Forest algorithm, known for its robustness in handling complex and high-dimensional data while identifying nonlinear patterns. The model was trained on a traffic accident dataset from Kaggle and incorporated important features, including driver age group, driving experience, type of vehicle, lighting and weather conditions, type of collision, number of vehicles involved, and casualties. The proposed system achieved 81% accuracy, 75% weighted precision, 81% weighted recall, and a weighted F1-score of 77%, demonstrating reliable performance in predicting accident severity levels Slight Injury, Serious Injury, and Fatal Injury. And providing useful insights for data-driven planning in traffic safety management.
Co-Authors Aprilisa Arum Sari Nurmalitasari Nurmalitasari