<![CDATA[PT. Saida Indra Panca is a company that manages outsourced labor, such as cleaning services, room attendants, and landscape maintenance. The company often faces difficulties in identifying employees eligible for training and development. Therefore, this study aims to compare the effectiveness of decision-making with and without clustering. The clustering method uses the K-Means algorithm, while the decision-making method uses TOPSIS. The research adopts the CRISP-DM approach, which includes business understanding, data collection, data preparation, modeling, evaluation, and deployment. Evaluation was conducted using ANOVA to compare the variance values of two groups: the first group with clustering and TOPSIS, and the second group with TOPSIS only. The evaluation resulted in an F-value of 5.553025 and a P-value < 0.05, indicating a significant difference between the group means. The study shows that the combination of K-Means and TOPSIS is superior to using TOPSIS alone, as it results in a more structured, efficient, and accurate decision-making process. Clustering helps group employee data based on specific characteristics, making the evaluation and ranking process more targeted. As a result, the company can improve HR management efficiency by up to 25% and enhance the accuracy in selecting employees for training. This approach provides deeper insights for developing effective data-driven HR strategies and supports better decision-making in employee management.]]>
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