Analysis of Student Mental Health Dataset Using Mining Techniques
- 1 Department of Information System, Universitas Bunda Mulia, Jakarta, Indonesia
- 2 Department of Accounting, Universitas Bunda Mulia, Jakarta, Indonesia
- 3 Department of Visual Communication Design, Universitas Bunda Mulia, Jakarta, Indonesia
Abstract
This study utilizes a decision tree model in RapidMiner to analyze a dataset from Kaggle, comprising 200 student records. Among these, 70 students reported mental health issues, while 130 did not. Strikingly, a significant majority of 58 out of the 70 students with mental health concerns do not seek assistance from professionals. This study underscores the pressing issue of underutilization of mental health services among students and offers practical solutions, such as enhancing awareness and education, improving access to mental health services, providing peer support, and addressing underlying issues. The research design includes data collection methods that maintained ethical standards and the decision tree model's application for analysis. This study's contribution lies in its identification of the prevalence of students with mental health issues who do not seek help and the proposed solutions to address this critical issue.
DOI: https://doi.org/10.3844/jcssp.2024.121.128
Copyright: © 2024 Yemima Monica Geasela, Devi Yurisca Bernanda, Johanes Fernandes Andry, Christian Kurniadi Jusuf, Samuel Winata, Lydia and Shierly Everlin. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Big Data
- Mental Health
- Educational
- Institutions
- Rapid Miner
- Decision Tree