KLASIFIKASI MENTAL MAHASISWA MENGGUNAKAN METODE MACHINE LEARNING

Abstract

The high level of stress and depression among students is a serious problem that must be faced, there are 971 suicides in Indonesia in 2023. Classifying the mental health conditions of students can provide effective assistance for students who have mental health problems and can also help the campus and family in identifying the condition of the student. In this study, we used an open dataset from kaggle that includes the mental health conditions of students and college students. The methods used are K-Nearest Neighbor (KNN) and Naive Bayes algorithms to find accuracy, precision, recall, and f1-score values. First, students will fill out a questionnaire. Next, the data from the questionnaire will be processed to group students into several different clusters based on pattern similarity. In the dataset there are 100 data that are processed and get the results of KNN measurements of Depression 80%, Panic 70% and Anxiety 85%,the results of Naive Bayes measurements of Depression 70%, Panic 75% and Anxiety 75%.