ANALISIS SPASIAL KLASTER KECELAKAAN LALU LINTAS MENGGUNAKAN ALGORITMA K-MEANS BERBASIS QGIS UNTUK IDENTIFIKASI BLACK SPOT DI KABUPATEN GUNUNGKIDUL - Lukky Astuti; Agus Sidiq Purnomo (Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, 2026)
Traffic accidents are a serious problem in Gunungkidul Regency, with more than 947 recorded incidents throughout 2025. This study aims to analyze spatial distribution patterns of accidents, identify risk cluster characteristics, determine black spot locations, and formulate mitigation recommendations. The method includes Risk Index calculation using Weighted Risk Assessment with five criteria (C1-C5), followed by the K-Means clustering algorithm validated using the Elbow Method to determine the optimal number of clusters (k=3) and the Silhouette Score (0.6645) to measure cluster quality, as well as QGIS-based spatial visualization. The results on 500 sample data show that the optimal number of clusters is k=3 with a Silhouette Score of 0.6645. Cluster 1 (low risk) includes 164 data (32.8%), Cluster 2 (medium risk) 273 data (54.6%), and Cluster 3 (black spot) 63 data (12.6%). Statistical validation using the Kruskal-Wallis test showed significant differences between clusters (H=419.5850; p<0.001). The main black spots are concentrated on the Wonosari-Yogyakarta Road, Patuk and Playen segments. This study produces specific mitigation recommendations for each risk cluster.