The Application of the K-Medoid Classification Method for Analyzing Crime Rates in South Sulawesi

Abstract

This research employs the k-medoid clustering method to analyze districts and cities in South Sulawesi based on their crime rates. As the population grows, employment opportunities tend to diminish, which can increase stress levels and, consequently, the likelihood of criminal behavior. To evaluate the distribution of criminal incidents across South Sulawesi, the k-medoid method is used to cluster regions. Unlike other clustering methods, k-medoid utilizes the median as the cluster center (medoid), which enhances its robustness against outliers. Specifically, the Partitioning Around Medoids (PAM) algorithm is applied, where initial objects are randomly selected to represent clusters. If the error value is high, the cluster centers are adjusted until the error is minimized. The dataset comprises crime incidence data for South Sulawesi in 2020, focusing on various types of crime. The analysis identified an optimal number of three clusters based on the Silhouette coefficient. Cluster 1 includes 11 regions, Cluster 2 consists of 8 regions, and Cluster 3 contains 5 regions. These clusters provide a comprehensive overview of the crime conditions across different regions within each cluster.