CLUSTERING DALAM MENENTUKAN TINDAK LANJUT HASIL ANNUAL CHECK MENTAL HEALTH DENGAN ALGORITMA K-MEANS - Lorensius Bima Ade Prasetya; Sasa Ani Arnomo (Universitas Putera Batam, 2025)
Addressing mental health issues has become a priority for the Indonesian government due to the rising prevalence of depression and anxiety, especially in Riau Archipelago Province, which reported as one of the provinces with the highest suicide rates in the country. The World Health Organization has pointed out the deficiencies in data collection, reporting, and knowledge management related to mental health. PT XYZ has collected mental health data through annual medical check-ups utilizing the DASS-21 scale, but this data has not been analyzed to extract useful and actionable insights for management. This research aims to profile the mental health of PT XYZ workers using annual medical check-up data from June to September 2023, involving a total of 3,699 workers. By implementing the K-Means clustering algorithm based on three variables—depression, anxiety, and stress—the analysis revealed that 242 workers were identified in a cluster with severe mental health conditions, 1,271 in a cluster with moderate conditions, and the remaining 2,186 with mild conditions in a cluster.