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All Journal Promotif: Jurnal Kesehatan Masyarakat Community Development Journal: Jurnal Pengabdian Masyarakat PROFICIO: Jurnal Pengabdian Masyarakat Journal of Educational Innovation and Public Health Majority Science Journal Oshada
Indriyani, Yulis
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Health Professions Immunology & microbiology Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Journal : Journal of Educational Innovation and Public Health

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Model of Binary Logistic Regression to Predict Mental Health in College Students Indriyani, Yulis; Nur Susanti
Journal of Educational Innovation and Public Health Vol. 2 No. 1 (2024): Januari : Journal of Educational Innovation and Public Health
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/innovation.v2i1.2266

Abstract

Indonesia is entering a critical period for mental health. Research results from the The Indonesia National Adolescent Mental Health Survey (2022), around 15,5 million Indonesian teenagers experience mental health disorders. Students are part of late adolescence and are vulnerable to mental disorders. The binary logistic regression model is used to examine in more depth what variables have a significant effect. So, this research aims to predict mental health of students in the Faculty of Health Sciences, Pekalongan University. This type of research is observasional with a cross-sectinal design. Data were collected using the SRQ-20 via the Google Form platform using simple random sampling of 186 students. There were 130 students who indicated mental health disordes (69,9%). Simultaneously age, gender, major, semester level, mother’s educational level, father’s educational level, social support and dependence on using smartphone influence student’s mental health status (P Value<0,05). Even though only a few variables were partially significant, the precision percentage of the model that could be predicted correctly was 71,5%. The accuracy of the predicted model is quite good, namely student mental health status (y) = -3,720 + 2,403 (Major) – 1,980 (Mother’s Educational Level) + 1,444 (Father’s Educational Level) + 0,888 (Dependence on using Smartphone). Promotive and preventive interventions such as further screening and education to support student’s healthy mental health.
Co-Authors Adelia Solecha Putri Andieta Firly Ceciliane Anggitta Nur Haliza Anisa , Fadhilla Ardiana Priharwanti Dwindasari, Chelsy Rachel Erinda Keysha Mardiarini Evia Fardiana Falujatunnisa, Nirmala Fauziah Rayani Mahardika Hairil Akbar Irena Tri Hawa Karimah, Luthfiyatul Muhammad Syaikhon Yahya Novalista Ananda Putri Nur Susanti Pratiwi, Farah Angelina Rahmatika, Shilnia Ramadhani, Salsabilla Dwi Setiawan, Bagas Budi Soluna Faizal Riski Teguh Irawan Wahyuningsih Wahyuningsih Wihar, Anis Tiara Sari