K-MEANS CLUSTERING FOR DATA-DRIVEN TRAFFIC ACCIDENT IN EAST JAVA - Nafiiyah, Nur; Wardhani, Retno; Huda, Muhammad Nurul; Setyan, Ardath Prahara; Prakasa, Esa (STKIP PGRI Tulungagung, 2025)
Traffic accident analysis plays a crucial role in improving road safety and reducing accident rates. Besides enforcing traffic regulations and promoting driver vigilance, analyzing accident data can provide valuable insights into patterns and risk factors that contribute to accidents. This research aims to apply k-Means clustering to accident data in East Java from 2016 to 2020 in order to identify hidden patterns based on victim age, victim type, vehicle type, gender, and accident causes. The clustering process categorizes most variables into two groups (low and high), while victim age is divided into three groups (young, middle, and older). The results reveal distinct accident patterns across age groups and victim types, with high accident clusters dominated by young drivers and motorcyclists. These findings provide insights into the characteristics of high-risk groups and can serve as a reference for designing more targeted road safety policies and preventive strategies.