<![CDATA[This study aims to identify mental health profile segmentation among employees based on work productivity indicators in the context of working from home (WFH) using the K-Means clustering algorithm. This study uniquely integrates mental health and productivity indicators into an unsupervised clustering framework. A cross-sectional method was conducted on 100 employee respondents with 10 main variables, analysed using K-Means with four optimal cluster evaluation methods. The results identified four distinct segments: Low WFH Adaptation (25%), High WFH Enthusiasts (30%), Mixed Preference (25%), and Office Preference (20%), with Silhouette Score validation of 0.623 and Davies-Bouldin Index of 0.967. The main findings reveal the paradox of High WFH Enthusiasts, who have the highest productivity (93%) but the highest mental health risk (1.90). This segmentation provides a practical framework for developing personalised mental health intervention strategies in employee management in the remote working era.]]>
