<![CDATA[Crime is one of the aspects that can influence national stability and security. In this study, crime data is used to cluster crime-prone areas and is considered whether these areas require extra surveillance or not. This research employs the K-means and Fuzzy C-Means methods. The K-means method groups data based on the similarity of data with cluster centroids. This algorithm is relatively efficient in complexity and easy to understand. K-means explicitly allocates data to specific clusters. On the other hand, Fuzzy C-Means is capable of placing a data point that lies between two or more other clusters into a single cluster. This is due to each data point having a degree of membership to determine its grouping, making the chances of failure to converge or stable cluster centers very low. The optimal number of clusters is selected using validation through the Davies Bouldin Index and Calinski Harabasz Index. The research results indicate that clustering crime-prone areas using both methods yield the same outcome of 2 clusters. Cluster 1 exhibits a higher crime rate compared to cluster 2, as indicated by the higher average values of group members. The lowest Davies Bouldin Index is 1.04948, and the highest Calinski Harabasz Index is around 24.36783.]]>
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