Penerapan Logika Fuzzy Sugeno untuk Deteksi Tingkat Depresi Kerja Karyawan - Daffa, Muhammad; Sriani, Sriani (Jurnal Teknologi Sistem Informasi dan Aplikasi, 2023)
The problem of depression levels among employees in a company can have a negative impact on overall performance. Therefore, the company needs additional information as a tool to detect employees' depression levels early and gain an understanding of their level of depression. Researchers applied fuzzy Sugeno to build a model for detecting employees' depression levels based on their psychological data. A dataset including variables such as weight and height, which generate Body Mass Index (BMI) values, and scores from the Patient Health Questionnaire-9 (PHQ-9), was used as input for the model. The data was then transformed into fuzzy set concepts, and fuzzy rules were built based on existing domain knowledge. The model was constructed using 60 employee respondents who had completed the questionnaire. The study utilized the Matlab application, which provides accurate results. The research findings indicate that the fuzzy Sugeno logic model is capable of detecting employees' depression levels, with results showing (63.3%) of employees experiencing mild depression, followed by (31.7%) experiencing moderate depression, and a small percentage of employees experiencing severe depression (5%).