Implementasi Metode Fuzzy Tsukamoto Untuk Deteksi Dini Tingkat Depresi Mahasiswa Yang Sedang Menempuh Skripsi (Studi Kasus: Fakultas Ilmu Komputer Universitas Brawijaya) - Priscillia Pravina Putri Sugihartono; Nurul Hidayat; Tibyani Tibyani (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2020)
Mental health is a state of the psychological and emotional condition of an individual that is functioning well. An individual is considered mentally healthy if this individual can perform well at coping stress and anxiety, adjusting or having a good and stable emotional state, and do activities and functions productively. When an individual acts otherwise, this individual is likely to have a mental illness. Mental illness is also considered as an invisible illness which causes many people aren't aware of their own mental health condition. One of the mental illnesses that many people are suffering from is depression. This paper is aimed to learn about depression severities on the Faculty of Computer Science students who are currently conducting their theses by utilizing a machine-learning method. University Students Depression Inventory (USDI) is utilized to examine depression severity based on the three correlated factors which are motivation factor, academic factor, and lethargy factor. The machine-learning algorithm that was used in this research is Fuzzy Tsukamoto. A total of 65 student cases were examined and it resulted the accuracy of 76,92%.