Abstract: Traffic accidents pose a significant public health and safety challenge in Indonesia, ranking fifth globally in terms of traffic fatality rates. This study aims to identify patterns in traffic accident data to inform effective mitigation strategies. Utilizing the K-Medoids algorithm, we clustered traffic accident data from the Indonesian Central Bureau of Statistics for the period 1992–2022. Prior to clustering, rigorous data preprocessing was conducted to ensure accuracy. The K-Medoids algorithm successfully partitioned the data into distinct clusters, revealing variations in accident patterns across different regions of Indonesia, including disparities in accident frequency and severity. This research provides valuable insights for policymakers and transportation authorities to develop targeted interventions and improve road safety in Indonesia. Additionally, this study successfully applied the K-Medoids algorithm to cluster traffic accident data in Indonesia using data from 2018 to 2022.

Handrizal , Hayatunnufus and Maryo Christopher Davinci Nababan, “Improving Road Safety in Indonesia: A Clustering Analysis of Traffic Accidents Using K-Medoids” International Journal of Advanced Computer Science and Applications(IJACSA), 16(3), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160358

@article{2025,
title = {Improving Road Safety in Indonesia: A Clustering Analysis of Traffic Accidents Using K-Medoids},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160358},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160358},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {3},
author = {Handrizal and Hayatunnufus and Maryo Christopher Davinci Nababan}
}