Implementation of the K-Means Clustering Algorithm for Segmenting Employee Mental Health Profiles Based on Work Productivity Indicators - Rahman, Maulia; Leman, Dedi (Journal of Computer Networks, Architecture and High Performance Computing, 2025)
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.