MAPPING CRIME-PRONE AREAS USING PRINCIPAL COMPONENT ANALYSIS (PCA) – CENTROID LINKAGE - Yossy Candra; Andrea Tri Rian Dani (Multica Science and Technology (ACCREDITED-SINTA 5), 2025)
Cluster analysis is a method employed to categorize data or objects according to their degree of resemblance. Centroid linkage is an algorithm that can be utilized in the grouping process. Centroid Linkage employs a hierarchical methodology that categorizes things into tiers according to their degree of similarity. Nevertheless, multicollinearity issues frequently arise in cluster analysis scenarios. Optimization of the centroid linkage technique through principal component analysis (PCA) diminishes research variables and generates a new principal component to address the issue of multicollinearity. To assess the validity of the clusters, the Silhouette Coefficient (SC) was utilized. The case study included characteristics deemed pertinent to crime issues in 34 provinces in Indonesia in 2021. The analysis yielded six principal components (PCs) with eigenvalues of one or above. The results from the Centroid Linkage algorithm indicated that the optimal number of clusters is 2, with a silhouette coefficient (SC) value of 0.61, signifying a well-structured and effective clustering arrangement. The attributes and delineation of each established cluster can yield insights for identifying crime-prone regions.