ANALYSIS OF MENTAL HEALTH CLASSIFICATION IN FINAL-YEAR FEMALE UNIVERSITY STUDENTS COMPLETING THEIR THESIS USING THE SUPPORT VECTOR MACHINE ALGORITHM CASE STUDY UNIVERSITY OF DARUSSALAM GONTOR

Kirani, Anisa (2025) ANALYSIS OF MENTAL HEALTH CLASSIFICATION IN FINAL-YEAR FEMALE UNIVERSITY STUDENTS COMPLETING THEIR THESIS USING THE SUPPORT VECTOR MACHINE ALGORITHM CASE STUDY UNIVERSITY OF DARUSSALAM GONTOR. S1 Undergraduate thesis, Universitas Darussalam Gontor.

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Abstract

Overall health depends on mental health or psychological well-being. Mental and physical well-being are equally vital. One in two young people under the age of 25 will experience mental health issues at some point, and 75% of mental illnesses begin before the age of 25. Female university students are among those at the highest risk for mental health issues. Typically, students in their early and final semesters experience a high level of academic anxiety. One of the main factors causing psychological distress in students is the final project or thesis. This study aims to classify mental health levels, namely stress and anxiety, by training a classification model that uses the Support Vector Machine algorithm. The dataset used in this study is derived from a questionnaire distributed to final-year female students working on their thesis. The dataset consists of 249 records and is divided into two datasets for stress and anxiety classification. The results of this study show the highest accuracy in the stress classification dataset using the RBF and polynomial kernels, reaching 68% with the RBF kernel at gamma 1 and C 100. Meanwhile, the highest accuracy in the anxiety classification dataset reached 50%, achieved with the polynomial kernel at degrees 3 and C 100. The application of the best model indicates that the most influential features in the Stress and Anxiety datasets are the Literature Review, Support System, and Analysis Method. The accuracy of the obtained data can be used as a standard for upcoming studies using more complex data. Future studies are expected not only to consider external factors as features determining student’s levels of stress and anxiety but also to add internal factors.

Item Type:	Thesis ( S1 Undergraduate )
Additional Information:	Skripsi : Anisa Kirani NIM : 422021618013
Uncontrolled Keywords:	Mental Health; Classification; Support Vector Machine
Subjects:	Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 000 - Ilmu komputer, informasi dan pekerjaan umum 23rd Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 000 - Ilmu komputer, informasi dan pekerjaan umum Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 004 - Pemrosesan data dan ilmu komputer 23rd Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 004 - Pemrosesan data dan ilmu komputer Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 005 - Pemrograman komputer, program dan data 23rd Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 005 - Pemrograman komputer, program dan data
Divisions:	Fakultas Sains dan Teknologi UNIDA Gontor > Teknik Informatika
Depositing User:	2021 Anisa Kirani
Date Deposited:	13 Feb 2025 11:08
Last Modified:	13 Feb 2025 11:08
URI:	http://repo.unida.gontor.ac.id/id/eprint/5395

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