<![CDATA[Crime is a phenomenon that significantly impacts society, necessitating mapping efforts that can be utilized for further analysis. Clustering, as a data analysis technique, groups objects based on similarities or differences in their characteristics. This approach enhances the understanding of data by identifying patterns and relationships between criminal events, such as crime type, time, and location. By clustering crime data based on similar characteristics, authorities can make more effective and efficient decisions in crime prevention and control. However, selecting too many attributes can negatively affect clustering performance. To address this issue, this study applies Information Gain reduction to reduce data dimensionality by eliminating attributes with low informational contribution. Additionally, three clustering methods K-Medoid, K-Means, and X-Means are compared to evaluate their performance. The concept of Information Gain is also integrated to optimize cluster formation, measuring how much an attribute contributes to distinguishing objects within a cluster. By leveraging Information Gain, this study aims to identify the most relevant and influential attributes in forming clusters that accurately represent crime data characteristics. Furthermore, the number of clusters generated is evaluated using the Davies-Bouldin Index (DBI). The results indicate that the K-Means algorithm outperforms the other two methods, achieving the best clustering quality with an optimal number of clusters (k = 6) and the lowest DBI value.]]>
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