(untitled) (Elkom: Jurnal Elektronika dan Komputer, 2025)
Traffic accidents in Brebes Regency represent a critical concern due to the high frequency of incidents that occur in the area. This research seeks to determine areas vulnerable to accidents by employing the K-Means Clustering algorithm, which supports data-based decision-making processes. The central issue explored in this study is how the K-Means algorithm can be implemented to group accident-prone zones and raise public awareness regarding road safety. The methodology involves data acquisition through literature reviews, direct observations, and interviews, followed by the use of the K-Means algorithm to classify accident data based on the number of occurrences, fatalities, and injuries. The findings show that the K-Means algorithm effectively clusters accident-prone locations into three distinct risk levels: high, moderate, and low. As a result, this categorized information can assist relevant authorities in enhancing traffic safety measures and educating the community about high-risk areas. The outcomes of this research are expected to contribute to more informed and strategic traffic safety policy development in Brebes Regency.