Klasifikasi Kesehatan Mental Mahasiswa dengan Algoritma J48 dan Hubungan Atribut Demografis serta Akademis

Abstract

The college years are a period of significant development in various aspects, including physical, psychological, and intellectual growth. Students often face mental health issues such as depression, anxiety, and panic attacks, which frequently go untreated and can persist into adulthood. This study aims to analyze the mental health of college students using the J48 algorithm to classify the target attribute "Did you seek any specialist for treatment?". The dataset used was sourced from a public repository, consisting of 101 instances. Through the stages of data collection, preprocessing, classification with the J48 algorithm, attribute correlation analysis, and comorbidity identification, the study found that 6% of students required specialist treatment, while 94% did not. Further analysis revealed that mental health issues are more prevalent among women and first-year students. Depression affects 82.86% of women, anxiety affects 70.59% of women, and panic attacks affect 75.76% of women. The study also identified comorbidities where some students experienced more than one mental health issue. The findings highlight the importance of social support and a conducive learning environment to improve students' mental well-being. These findings can be used to develop more effective interventions to support students' mental health and academic success.