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All Journal JURNAL NASIONAL TEKNIK ELEKTRO Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JTERA (Jurnal Teknologi Rekayasa) INTEK: Informatika dan Teknologi Informasi TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Jurnal Sosial dan Teknologi Jurnal Abdimas ADPI Sains dan Teknologi Jurnal Teknologi dan Manajemen Eksergi: Jurnal Teknik Energi Electrician : Jurnal Rekayasa dan Teknologi Elektro SOROT: Jurnal Pengabdian Kepada Masyarakat Journal Of Artificial Intelligence And Software Engineering Journal of Global Engineering Research & Science (J-GERS) Journal Of Renewable Energy Engineering SNTE
Devan Junesco Vresdian
Teknologi Informasi, Telkom University, Jakarta, Indonesia
Aerospace Engineering Humanities Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Other

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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 Abdullah, Muhammad Irsyad Abdurrahman, Naufal Abir Oktaviani Agnemas Yusoep Islami Agung Pangestu Ahmad Syafei Nursuwanda Al-Humairi, Safaa Najah Sahud Al-Humairi, Safaa Najah Saud Al-yousif, Shahad Alviandra Naufal Azis Caesarea Ananda Hapsari, Anindya Andreansyah, Teddy Anindya Ananda Hapsari Anindya Hapsari Annisa Desianty Ariep Jaenul Ariep Janeul Arisa Olivia Putri Ayu Nurul Haryudiniarti Brainvendra Widi Dionova Budi Priyono, Sigit Cahyo Wibisono Suryo Jati Dendy Prasetyo Dian Nugraha Eko Supriyono, Eko Farhan Dwi Ananda Putra Feri Nugroho Halimatuz Zuhriyah Hamzah Hamzah Hutagalung, Nirmalasari Ikmal Ikmal Ikmal Jaharadak, Adam Amril Legenda Prameswono Pratama legenda pratama M.N.Mohammed Manfaluthy, Mauludi Manfaluthy, Mauludy Marcelino Setiyo Mufti Maulana, Muhamad Waldi Mohammed, M. N. Muhammad Assadullah N. Abdulrazaq, Mohammed Nafiulana Tri, Anggita Nursuwanda, Ahmad Syafei Permana, Muhammad Zidan Regi Quinnsha Thara Diva Rizki Surya Permana Rosyid Ridlo Al-Hakim Setiawan, Antonius Darma Setyono, Rizka Sabila Sinka Wilyanti Sinta Restuasih Sobar, Dedi Sriyadi Sriyadi Sundari, Delta Suryo Jati, Cahyo Wibisono Trisnaaji, Pius Adrian Widang Muttaqin Wilyanti, Sinka Yasya Khalif Perdana Saleh Zuhriyah, Halimatuz