<![CDATA[PT XYZ is a systems integration company with a diverse workforce comprising permanent employees, university interns, and vocational high school (VHS) interns. Differences in employment status and individual characteristics provide a context for exploring human resource segmentation based on individual experiences and performance assessments. This exploratory study aims to identify human resource segments using the K-Means Clustering algorithm and describe the distribution of cluster membership across employment status categories. The study involved 18 respondents. Before clustering was performed, the data were tested for validity and reliability, yielding Cronbach’s alpha coefficients of 0.946 for X1 and 0.767 for X2. The optimal number of clusters was determined using the Elbow Method and evaluated using the Silhouette Score. The results revealed four clusters representing the following performance categories: Excellent, Very Good, Fairly Good, and Needs Improvement. Cross-tabulation analysis was subsequently used to describe the distribution of employment status across the resulting clusters. These findings provide preliminary insights into workforce segmentation among respondents at PT XYZ and may serve as a reference for further discussions on human resource management practices]]>
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