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Implementation of the Term Frequency-Inverse Document Frequency Method for Mental Health Classification Using Algorithm Support Vector Machine

Recursive Journal of Informatics · Vol. 3 No. 2 (2025) · pp. 93–98

DOI
10.15294/rji.v3i2.1921
ISSN
2986-6588, 2963-5551
Published
2025-10-17
Publisher
Universitas Negeri Semarang
Language
eng

Abstract

Abstract. Mental health is a person's emotional, social and psychological condition. A person's mental health level can be influenced by emotional experiences, behavior, environment and family educational background. A person's psychological well-being can be influenced by a person's behavior, where they live, the education they receive, and their emotional experiences. It is important not to underestimate the existence of mental health disorders because the number of cases is currently increasing. Purpose: Using SVM algorithm and TF-IDF method can produce good accuracy for classification text. Therefore this research aims to determine the implementation of the use of the TF-IDF method and the SVM algorithm in mental health classification and to determine the accuracy results of using these methods. Study Method/Design/Approach: The methods used in the research this for the mental health classification is Term Frequency-Inverse Document Frequency used in the vectorization process to convert text into a numerical representation, as well as using the Support Vector Machine algorithm in modeling. The dataset used is the Mental Health Corpus dataset obtained from the Kaggle website. This dataset consists of two classes containing text and labels totaling 27,977 data. Before applying the model, preprocessing is carried out first, namely cleaning the text using stopword removal and stemming. After cleaning the text, the next process is vectorization using CountVectorizer and TF-IDF. Results/Findings: In this study the SVM algorithm was used four kernels, namely the linear kernel, the RBF kernel, the polynomial kernel, and the later sigmoid kernel get the best accuracy results on the RBF kernel if compared to with other kernels. Accuracy results obtained _ of 92.62%, value precision of 92.64%, value recall 92.62%, and value f1-score 92.62%. Novelty/Originality/Value: So, it can be concluded that the application of the SVM algorithm and the TF-IDF method is possible used for classification mental health results mark high accuracy.

Keywords

Collaboration network of the authors

40 authors · 103 co-authorship links · 19 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • State University of Semarang
  • Institut Teknologi Kalimantan
  • Tun Hussein Onn University of Malaysia
  • Universitas PGRI Palembang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Endang SugihartiState University of Semarang5
Florentina Yuni AriniState University of Semarang3
Rochmad RochmadAffiliation unknown3
Amin SuyitnoAffiliation unknown3
Alamsyah AlamsyahInstitut Teknologi Kalimantan2
Anggyi Trisnawan PutraAffiliation unknown2
Novanka Agnes SekartajiAffiliation unknown2
Irfan Fajar MuttaqinAffiliation unknown2
Ramadhan Ridho ArrohmanAffiliation unknown2
Dwijanto DwijantoAffiliation unknown2
Putriaji HendikawatiAffiliation unknown2
Mashuri MashuriAffiliation unknown2
Budi PrasetiyoState University of Semarang1
Much Aziz MuslimTun Hussein Onn University of Malaysia1
Jumanto JumantoState University of Semarang1
Rofik RofikState University of Semarang1
Arif WidiyatmokoState University of Semarang1
Stephani Diah PamelasariState University of Semarang1
Arka YanitamaState University of Semarang1
Melissa Salma DarmawanState University of Semarang1
Aji SaputraUniversitas PGRI Palembang1
Desy Fitria AstutianingtyasAffiliation unknown1
Syafa Nabilah SyahputraAffiliation unknown1
Athoillah AthoillahAffiliation unknown1
Kholiq BudimanAffiliation unknown1
zaenal abidinAffiliation unknown1
Anggoro Sakti AnindhitoAffiliation unknown1
Fajar SujitoAffiliation unknown1
Anita Wahyu MelindaAffiliation unknown1
Isa AkhlisState University of Semarang1
Achmad Wahyu HidayatAffiliation unknown1
Dian Rahman SyahputraAffiliation unknown1
Safit FirmansyahAffiliation unknown1
Anggil AgustaAffiliation unknown1
Fachrizal Ahdy AndoyoAffiliation unknown1
Margareta Ayu RiantikAffiliation unknown1
Dandi Indra WijayaAffiliation unknown1
Hanif Nur CahyaniAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/rji/oai, first harvested 6 Nov 2026, last seen 6 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Implementation of the Term Frequency-Inverse Document Frequency Method for Mental Health Classification Using Algorithm Support Vector Machine</dc:title>
	<dc:creator xml:lang="en">Minatika, Ilfa</dc:creator>
	<dc:creator xml:lang="en">Arifudin, Riza</dc:creator>
	<dc:subject xml:lang="en">Machine Learning</dc:subject>
	<dc:subject xml:lang="en">SVM</dc:subject>
	<dc:subject xml:lang="en">TF-IDF</dc:subject>
	<dc:subject xml:lang="en">Mental Health</dc:subject>
	<dc:subject xml:lang="en">Classification text</dc:subject>
	<dc:description xml:lang="en">Abstract. Mental health is a person's emotional, social and psychological condition. A person's mental health level can be influenced by emotional experiences, behavior, environment and family educational background. A person's psychological well-being can be influenced by a person's behavior, where they live, the education they receive, and their emotional experiences. It is important not to underestimate the existence of mental health disorders because the number of cases is currently increasing.
Purpose: Using SVM algorithm and TF-IDF method can produce good accuracy for classification text. Therefore this research aims to determine the implementation of the use of the TF-IDF method and the SVM algorithm in mental health classification and to determine the accuracy results of using these methods.
Study Method/Design/Approach: The methods used in the research this for the mental health classification is Term Frequency-Inverse Document Frequency used in the vectorization process to convert text into a numerical representation, as well as using the Support Vector Machine algorithm in modeling. The dataset used is the Mental Health Corpus dataset obtained from the Kaggle website. This dataset consists of two classes containing text and labels totaling 27,977 data. Before applying the model, preprocessing is carried out first, namely cleaning the text using stopword removal and stemming. After cleaning the text, the next process is vectorization using CountVectorizer and TF-IDF.
Results/Findings: In this study the SVM algorithm was used four kernels, namely the linear kernel, the RBF kernel, the polynomial kernel, and the later sigmoid kernel get the best accuracy results on the RBF kernel if compared to with other kernels. Accuracy results obtained _ of 92.62%, value precision of 92.64%, value recall 92.62%, and value f1-score 92.62%.
Novelty/Originality/Value: So, it can be concluded that the application of the SVM algorithm and the TF-IDF method is possible used for classification mental health results mark high accuracy.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-10-17</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/vnd.openxmlformats-officedocument.wordprocessingml.document</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/rji/article/view/1921</dc:identifier>
	<dc:identifier>10.15294/rji.v3i2.1921</dc:identifier>
	<dc:source xml:lang="en">Recursive Journal of Informatics; Vol. 3 No. 2 (2025): September 2025; 93-98</dc:source>
	<dc:source>2986-6588</dc:source>
	<dc:source>2963-5551</dc:source>
	<dc:source>10.15294/rji.v3i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/rji/article/view/1921/6207</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Recursive Journal of Informatics</dc:rights>
</oai_dc:dc>