IMPLEMENTASI K-MEANS CLUSTERING BERBASIS GIS DALAM MENGIDENTIFIKASI DAERAH RAWAN KECELAKAAN LALU LINTAS DI KOTA MATARAM: Implementation of GIS-Based K-Means Clustering for Identifying Traffic Accident-Prone Areas in Mataram City - Rohani, Rohani; Hasyim, Hasyim; Handayani, Siti Maulida (Ganec Swara, 2026)
The growth in the number of vehicles in Mataram City has led to increased traffic congestion and a higher risk of accidents each year. Data from the Mataram Police Department recorded 439 traffic accident cases in 2023. This study aims to identify and map accident-prone areas using the K-Means Clustering method integrated with a Geographic Information System (GIS). The analysis was conducted using a clustering approach to group areas based on their level of accident risk, taking into account the weighted number of incidents and casualties. The quality of the clustering results was evaluated using the Silhouette Coefficient to measure cluster accuracy. The data used in this study are secondary traffic accident data from the 2021–2024 period obtained from the Mataram Police Department, focusing on arterial and collector roads. The results show that several road segments are consistently categorized as accident-prone, namely Jalan Lingkar Selatan, Jalan Ahmad Yani, Jalan Jenderal Sudirman, Jalan Sandubaya, Jalan Saleh Sungkar, and Jalan Sriwijaya. The K-Means Clustering method produced three categories of areas: “Safe,” “Moderately Prone,” and “Prone,” with Silhouette Coefficient values ranging from 0.7 to 1.0, indicating a strong cluster structure. GIS-based visualization provides a clear depiction of the distribution of accident-prone areas and can serve as a basis for the government in formulating traffic safety policies in Mataram City