Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal Of Artificial Intelligence And Software Engineering
Ahmad Syafei Nursuwanda
Universitas Global Jakarta
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
A Comparative Study of Decision Tree, Logistic Regression, and Random Forest Models for Identifying Mental Health Issues Among University Students Ahmad Syafei Nursuwanda; Anindya Ananda Hapsari; Halimatuz Zuhriyah; Devan Junesco Vresdian
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9775

Abstract

Mental health is an important issue in Indonesia, particularly among university students who are vulnerable to anxiety due to academic pressure, life challenges, and emotional instability. This study compares the performance of three machine learning algorithms Decision Tree, Logistic Regression, and Random Forest for detecting anxiety among university students using Python. The results indicate that Logistic Regression achieved the highest accuracy of 90%, while Decision Tree and Random Forest each achieved 80% accuracy. Evaluation using 20% of the dataset for testing and validation with the Taylor Manifest Anxiety Scale (TMAS) showed that Logistic Regression correctly identified 4 out of 5 students with anxiety. These findings demonstrate that Logistic Regression is the most effective algorithm and has strong potential to support early anxiety detection through a data science–based approach.
Co-Authors Anindya Ananda Hapsari Devan Junesco Vresdian Halimatuz Zuhriyah