<![CDATA[Complex personnel data management is a strategic challenge for organizations, especially in government agencies such as the Banyuwangi Transportation Agency. This study aims to provide a solution to this problem by using the K-Means Clustering method. This technique allows grouping employee data based on key attributes, namely rank, position, and length of service. The research data was obtained from the Banyuwangi Transportation Agency personnel documents and processed using RapidMiner software to ensure the accuracy of the clustering results. The results of the study show that employee data can be grouped into two main clusters. These clusters reflect employee distribution patterns based on the characteristics of rank, position, and length of service, which can then be used to support strategic decision making, such as the preparation of employee training, promotion, and rotation policies. This study proves that the K-Means method is effective in analyzing complex personnel data and makes a significant contribution to increasing the efficiency of human resource management in government agencies.]]>
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