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COMPARING DEEP LEARNING AND MACHINE LEARNING FOR DETECTING FAKE NEWS ON SOCIAL MEDIA

Jurnal Ilmiah Ilmu Terapan Universitas Jambi · Vol. 9 No. 3 (2025) · pp. 1091–1103

DOI
10.22437/jiituj.v9i3.46370
ISSN
2580-2259, 2580-2240
Published
2025-08-10
Publisher
LPPM Universitas Jambi
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

One of the critical issues resulting from the increasing penetration of social media is the spread of fake news. This can damage public information and influence mass opinion, leading to conflict. To overcome this problem, machine learning and deep learning-based approaches have been continuously developed to detect fake news on various social media platforms automatically. This article aims to compare the effectiveness of these two approaches in detecting fake news. The methods used include the implementation of traditional machine learning algorithms, such as Support Vector Machines (SVM) and Random Forest, as well as deep learning-based approaches, including Long Short-Term Memory and Self-Organizing Maps. Datasets containing real and fake news from various social media sources are used to train and evaluate these models. Model performance is measured based on accuracy, precision, recall, and F1-score. This study aims to determine which approach is more effective and identify challenges in implementing these algorithms in a dynamic social media environment. The results obtained show that the Random Forest algorithm achieves an accuracy level of 100%, surpassing other algorithms, including Long Short-Term Memory with an F-1 Score of 97%, Self-Organizing Map with an F-1 Score of 96%, and Support Vector Machine with an F-1 Score of 92%.

Keywords

Collaboration network of the authors

18 authors · 67 co-authorship links · 25 across different organisations

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

  • Universitas Bina Darma
  • Universitas Labuhanbatu
  • Universitas 17 Agustus 1945 Jakarta
  • Sriwijaya University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Prihambodo Hendro SaksonoUniversitas Bina Darma3
Deni ErlansyahUniversitas Bina Darma3
Ferdi AdityaUniversitas Bina Darma3
Padel Mohammad AgamUniversitas Bina Darma3
Latius HermawanSriwijaya University3
Sunda ArianaUniversitas Bina Darma3
Hastari MayritaUniversitas Bina Darma3
Desy ArisandyUniversitas Bina Darma3
Mohamad FaroziUniversitas Bina Darma3
JemakmunAffiliation unknown1
Mukran RoniAffiliation 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://online-journal.unja.ac.id/index.php/JIITUJ/oai, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">COMPARING DEEP LEARNING AND MACHINE LEARNING FOR DETECTING FAKE NEWS ON SOCIAL MEDIA</dc:title>
	<dc:creator xml:lang="en">Andryani, Ria</dc:creator>
	<dc:creator xml:lang="en">Julian, Dedek</dc:creator>
	<dc:creator xml:lang="en">Syaputra, Rezki</dc:creator>
	<dc:creator xml:lang="en">Syazili, Ahmad</dc:creator>
	<dc:creator xml:lang="en">Rusli, Ahmad</dc:creator>
	<dc:creator xml:lang="en">Ramadan, Rahmat</dc:creator>
	<dc:creator xml:lang="en">Negara, Edi Surya</dc:creator>
	<dc:subject xml:lang="en">Deep Learning</dc:subject>
	<dc:subject xml:lang="en">Fake News</dc:subject>
	<dc:subject xml:lang="en">Machine Learning</dc:subject>
	<dc:subject xml:lang="en">Social Media</dc:subject>
	<dc:description xml:lang="en">One of the critical issues resulting from the increasing penetration of social media is the spread of fake news. This can damage public information and influence mass opinion, leading to conflict. To overcome this problem, machine learning and deep learning-based approaches have been continuously developed to detect fake news on various social media platforms automatically. This article aims to compare the effectiveness of these two approaches in detecting fake news. The methods used include the implementation of traditional machine learning algorithms, such as Support Vector Machines (SVM) and Random Forest, as well as deep learning-based approaches, including Long Short-Term Memory and Self-Organizing Maps. Datasets containing real and fake news from various social media sources are used to train and evaluate these models. Model performance is measured based on accuracy, precision, recall, and F1-score. This study aims to determine which approach is more effective and identify challenges in implementing these algorithms in a dynamic social media environment. The results obtained show that the Random Forest algorithm achieves an accuracy level of 100%, surpassing other algorithms, including Long Short-Term Memory with an F-1 Score of 97%, Self-Organizing Map with an F-1 Score of 96%, and Support Vector Machine with an F-1 Score of 92%.</dc:description>
	<dc:publisher xml:lang="en">LPPM Universitas Jambi</dc:publisher>
	<dc:date>2025-08-10</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://online-journal.unja.ac.id/JIITUJ/article/view/46370</dc:identifier>
	<dc:identifier>10.22437/jiituj.v9i3.46370</dc:identifier>
	<dc:source xml:lang="en">Jurnal Ilmiah Ilmu Terapan Universitas Jambi; Vol. 9 No. 3 (2025): Volume 9, Nomor 3, September 2025; 1091-1103</dc:source>
	<dc:source>2580-2259</dc:source>
	<dc:source>2580-2240</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://online-journal.unja.ac.id/JIITUJ/article/view/46370/21890</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Ria Andryani, Dedek Julian, Rezki Syaputra, Ahmad Syazili, Ahmad Rusli, Rahmat Ramadan, Edi Surya Negara</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>