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All Journal Research in the Mathematical and Natural Sciences Bulletin of Applied Mathematics and Mathematics Education
Adam, Dwi Putri Juniar
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Computer Science & IT Education Mathematics

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Klasifikasi Tingkat Depresi Mahasiswa Menggunakan Image Recognition dengan Support Vector Machine Abdussamad, Siti Nurmardia; Doholio, Nadya Pratiwi; Lasaleng, Wahyu Pratama; Usia, Putu Ayu Indah N.; Rahman, Mohamad Iswanto; Adam, Dwi Putri Juniar
Research in the Mathematical and Natural Sciences Vol. 4 No. 1 (2025): November 2024-April 2025
Publisher : Scimadly Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55657/rmns.v4i1.193

Abstract

Mental health problems in Indonesia are increasing, with university students being one of the groups vulnerable to depression due to academic pressure, social expectations, and exposure to negative information. Early detection of depression still relies on questionnaire methods that have limitations in objectivity and accuracy. Therefore, this research aims to develop a classification system for student depression using image recognition technology with Support Vector Machine (SVM). The system analyses students' facial expressions and combines them with questionnaire results to improve the accuracy of early depression detection. The results showed that out of 131 respondents, 74% experienced moderate depression, with academic pressure as the main factor. This finding is consistent with the condition of final-year students who face high academic loads. With this method, early detection of depression is more accurate than conventional methods, which can help intervene more quickly in dealing with student mental health crises.
Multicollinearity problem-solving with Jackknife Ridge Regression: A case study on slum conditions in Bone Bolango Adam, Dwi Putri Juniar; Nasib, Salmun K.; Adityaningrum, Amanda
Bulletin of Applied Mathematics and Mathematics Education Vol. 5 No. 1 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/bamme.v5i1.12759

Abstract

Slum conditions in Indonesia, particularly in Gorontalo Province's Bone Bolango District, are a significant challenge to sustainable development. This research aims to identify the key factors contributing to slum conditions in the strategic economic areas of Kabila, Suwawa, and Tilongkabila using Jackknife Ridge Regression (JRR) analysis to address multicollinearity and overfitting issues. Data from the Regional Development Planning Board (BAPPEDA) Bone Bolango District's 2023 document was used, with a sample of 40 urban villages and villages. The result showed that there is a high collinearity between two independent variables, necessitating the use of JRR. The JRR model identified seven independent variables significantly related to slum value. The regression model explained 83% of the variability in slum conditions. This study provides methodological depth through the JRR framework, which enables accurate slum analysis where traditional models (like OLS) tend to fall short. It emphasizes the need for Bone Bolango to prioritize its policy initiatives by focusing on the seven independent variables. Additionally, the framework demonstrates scalability, making it adaptable to other Indonesian provinces that face similar challenges with slum data.
Co-Authors Doholio, Nadya Pratiwi Lasaleng, Wahyu Pratama Rahman, Mohamad Iswanto Salmun K. Nasib Siti Nurmardia Abdussamad Usia, Putu Ayu Indah N.