Klasifikasi Tingkat Depresi Mahasiswa Menggunakan Image Recognition dengan Support Vector Machine

Siti Nurmardia Abdussamad; Nadya Pratiwi Doholio; Wahyu Pratama Lasaleng; Putu Ayu Indah N. Usia; Mohamad Iswanto Rahman; Dwi Putri Juniar Adam

doi:10.55657/rmns.v4i1.193

Authors

Siti Nurmardia Abdussamad Gorontalo State University
Nadya Pratiwi Doholio Universitas Negeri Gorontalo, Indonesia
Wahyu Pratama Lasaleng Universitas Negeri Gorontalo, Indonesia
Putu Ayu Indah N. Usia Universitas Negeri Gorontalo, Indonesia
Mohamad Iswanto Rahman Universitas negeri Gorontalo, Indonesia
Dwi Putri Juniar Adam Universitas Negeri Gorontalo, Indonesia

DOI:

https://doi.org/10.55657/rmns.v4i1.193

Keywords:

Depression, Image Recognition, Support Vector Machine

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.

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