Research Article Open Access

Analysis of Student Mental Health Dataset Using Mining Techniques

Yemima Monica Geasela1, Devi Yurisca Bernanda1, Johanes Fernandes Andry1, Christian Kurniadi Jusuf1, Samuel Winata1, Lydia2 and Shierly Everlin3
  • 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.

Journal of Computer Science
Volume 20 No. 1, 2024, 121-128

DOI: https://doi.org/10.3844/jcssp.2024.121.128

Submitted On: 23 August 2023 Published On: 22 December 2023

How to Cite: Geasela, Y. M., Bernanda, D. Y., Andry, J. F., Jusuf, C. K., Winata, S., Lydia, . & Everlin, S. (2024). Analysis of Student Mental Health Dataset Using Mining Techniques. Journal of Computer Science, 20(1), 121-128. https://doi.org/10.3844/jcssp.2024.121.128

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Keywords

  • Big Data
  • Mental Health
  • Educational
  • Institutions
  • Rapid Miner
  • Decision Tree