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All Journal Ability : Journal of Education and Social Analysis JagoMIPA: Jurnal Pendidikan Matematika dan IPA COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Indonesia Sosial Teknologi Jurnal Indonesia Sosial Sains Journal Research of Social Science, Economics, and Management Jurnal Pendidikan Indonesia (Japendi) Jurnal Teknik Indonesia Nursing Arts Hospitality Management and Operations Journal Jurnal Farmasi Sains dan Terapan (Journal of Pharmacy Science and Practice) Journal of Artificial Intelligence Research
Solikhah, Mar’atus
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Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Other

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THE EFFECT OF MACHINE LEARNING ALGORITHMS ON HOAX DETECTION ON SOCIAL MEDIA: IMPLICATIONS FOR NATIONAL INFORMATION SECURITY Solikhah, Mar’atus
Journal of Artificial Intelligence Research Vol. 1 No. 1 (2025): Journal of Artificial Intelligence Research
Publisher : Asosiasi Persatuan Pengusaha Muda Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64910/jouair.v1i1.7

Abstract

The spread of hoaxes on social media has become a serious threat to national information security, considering the large number of people who depend on social media as a source of information. This misinformation not only has an impact on public perception but also disrupts social and political stability. This study aims to test the effectiveness of machine learning algorithms, especially Natural Language Processing (NLP), Neural Networks, and Decision Tree, in detecting hoaxes on social media and analyzing their implications for national information security. The method used is a quantitative approach with experimental and comparative analysis of the three algorithms. The data was collected through web scraping from social media platforms and analyzed using the Confusion Matrix to assess accuracy, precision, recall, and F1-score. The results showed that NLP had the highest accuracy, reaching 92.7%, followed by Neural Networks with 90.1% and Decision Tree at 86.3%. In addition, the increase in hoax detection is directly proportional to the decrease in security incidents related to disinformation, indicating the important role of machine learning algorithms in maintaining national information stability. These findings support the implementation of hoax detection algorithms as part of a more comprehensive information security policy.
Co-Authors Ainul Kholipah, Siti Amelia Amelia Asri Arbie Baitul Jannah, Khofifah Indah Dwi Putri Pramesti, Ginna Novarianti Faransisca, Vika Fauziyyah, Ghina Fransisca, Vika Hossain, Md. Jakir Kholipah, Siti Ainul Lena Magdalena, Lena Luthfiah Luthfiah Masrid Pikoli Muawanah Muawanah Muhammad Hatta Rehman, Atif Rizqi Alvian Fabanyo Sairmau, Gerson Titus Sundari Sundari Tamamiyah, Lutfiyatut Teguh Widodo Widya Widya, Widya Yusuf, Frita Fauzi