Deteksi Dini Depresi Mahasiswa Tingkat Akhir Menggunakan Algoritma Naïve Bayes dan Instrumen PHQ-9 - Sadikin, Muhammad; Dwiki Putri, Dini Ridha; Amanda, Azrifirizky (Building of Informatics, Technology and Science (BITS), 2025)
This study aims to analyze the performance of the Naïve Bayes algorithm in an early detection system for depression levels among final-year university students using the PHQ-9 instrument. The classification is conducted in a multi-category manner, grouping respondents into normal, mild depression, moderate depression, and severe depression categories based on the results of the PHQ-9 questionnaire. The main issues addressed in this research are the low awareness of depression symptoms and the need for a screening tool that is fast, lightweight, and accessible. Involving 188 respondents, data were collected through the PHQ-9 questionnaire and subsequently processed using the Naïve Bayes method. Model evaluation was performed using a confusion matrix and 10-fold cross-validation, resulting in an accuracy of 87.23%, a weighted average precision of 87.54%, and a weighted average recall of 87.25%. These findings demonstrate that Naïve Bayes can classify depression levels with high and stable accuracy, particularly in the moderate and severe depression categories. This study recommends the use of the Naïve Bayes algorithm as the basis for developing a web-based screening system that can be utilized by higher education institutions as a self-assessment and systematic early detection tool for depression disorders.