Klasifikasi Tingkat Depresi Mahasiswa Menggunakan Algoritma Decision Tree C4.5 - Moza Adhelia; Giescha Ramadhani Wiwenar; Hasbi Shohibil Wafa (Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang, 2026)
Depression among college students has become an increasingly common problem due to academic pressure, emotional distress, and an unbalanced lifestyle. This condition often goes undetected in its early stages, necessitating a method that can assist in identifying the severity of depression among students. This study uses the C4.5 Decision Tree method to classify the level of student depression based on several variables, such as sleep quality, academic workload, age, and academic performance. The data obtained was analyzed to identify the most influential factors in the classification process. The results of the study indicate that sleep quality and academic workload are the most influential factors in determining students’ depression levels. The resulting model was able to group the data with a reasonably high level of accuracy according to the characteristics of each class. The C4.5 method can be used to support the early detection of student depression because it can clearly and easily demonstrate the relationships between variables. This research can still be further developed by improving data quality and adjusting the method to optimize classification results.