IDENTIFIKASI POLA KECELAKAAN LALU LINTAS DENGAN K-MEANS CLUSTERING - Bandhaso, Victor; Wati, Masna; uddin, Havil (STMIK Sinar Nusantara, 2026)
Traffic accidents represent a complex issue with significant social and economic impacts. This study aims to identify temporal patterns of traffic accidents based on temporal and demographic attributes using the K-Means Clustering algorithm applied to 9,659 accident records in Central Java Province in 2024. Time attributes were converted to decimal format, while occupational data for the involved parties were transformed into numerical codes to enable clustering analysis. The K-Means Clustering algorithm was then employed to generate cluster models. Cluster 0 is characterized by an afternoon peak in incident time around 18.10, with the closest encoded occupational category corresponding to TNI–POLRI personnel. Cluster 1 consists of an average incident occurring at 06.26, predominantly involving homemakers. Cluster 2 is dominated by homemakers, with incidents generally occurring around 17.03. Cluster 3 shows the dominance of TNI–POLRI personnel, with incidents most frequently occurring at 07.19. These findings indicate that the most frequently involved occupational groups are military/police personnel and homemakers, both of which exhibit high mobility during peak hours and also threaten officers who are supposed to maintain traffic order.