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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) · hlm. 1091–1103

DOI
10.22437/jiituj.v9i3.46370
ISSN
2580-2259, 2580-2240
Terbit
2025-08-10
Penerbit
LPPM Universitas Jambi
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

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%.

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  • Universitas Bina Darma
  • Sriwijaya University
  • Universitas Labuhanbatu
  • Universitas Muhammadiyah Palembang
  • Universitas 17 Agustus 1945 Jakarta
  • Diponegoro University
  • Universitas Islam Negeri Raden Fatah Palembang
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Rekan penulisOrganisasiArtikel bersama
Latius HermawanSriwijaya University4
Prihambodo Hendro SaksonoUniversitas Bina Darma3
Deni ErlansyahUniversitas Bina Darma3
Ferdi AdityaUniversitas Bina Darma3
Padel Mohammad AgamUniversitas Bina Darma3
Sunda ArianaUniversitas Bina Darma3
Hastari MayritaUniversitas Bina Darma3
Desy ArisandyUniversitas Bina Darma3
Mohamad FaroziUniversitas Bina Darma3
JemakmunAfiliasi tidak diketahui1
Mukran RoniAfiliasi tidak diketahui1
Rahmat GernowoDiponegoro University1
Surya Negara EdiAfiliasi tidak diketahui1
Nurhachita NurhachitaUniversitas Islam Negeri Raden Fatah Palembang1
Deris StiawanSriwijaya University1
Rizma Adlia SyakurahSriwijaya University1
Indri RamayantiUniversitas Muhammadiyah Palembang1
Meilinda MeilindaSriwijaya University1
Muhammad FahmiUniversitas Muhammadiyah Palembang1
Muhammad Qurhanul RizqieSriwijaya University1
Dedy HermantoUniversitas Multi Data Palembang1

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Sumber metadata: OAI-PMH (metadata jurnal) https://online-journal.unja.ac.id/index.php/JIITUJ/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (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>