Published: 2025-04-01
Analisis Big Data untuk Deteksi Hoaks dan Disinformasi di Platform Berita Online
DOI: 10.35870/jtik.v9i2.3859
Downloads
Abstract
In the digital era, the spread of hoaxes and disinformation on online news platforms is a serious challenge that can affect public opinion and social stability. This research aims to analyze the application of Big Data technology in automatically detecting hoaxes and disinformation. The methods used include data collection from various online news sources, text processing using Natural Language Processing (NLP), and the application of machine learning algorithms to classify news based on their level of credibility. The dataset used includes news from various categories, which are validated with trusted sources. The results show that the combination of Big Data, NLP, and machine learning techniques can improve the accuracy of hoax detection with a high success rate. This study is expected to contribute to the development of a fake news detection system that is more effective and adaptive to the trend of information dissemination in the digital world.
Keywords
Big Data ; Hoaks ; Missinformation ; Natural Language Processing
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 9 No. 2 (2025)
-
Section: Computer & Communication Science
-
Published: %750 %e, %2025
-
License: CC BY 4.0
-
Copyright: © 2025 Authors
-
DOI: 10.35870/jtik.v9i2.3859
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
Mitranikasih Laia
Universitas Nias Raya, Kabupaten Nias Selatan, Provinsi Sumatera Utara, Indonesia.
Ayuliana
Universitas Bina Nusantara, Kota Jakarta Barat, Daerah Khusus Ibukota Jakarta, Indonesia.
Wasiran
Universitas Papua Madani Jayapura, Kota Jayapura, Provinsi Papua, Indonesia.
Muhammad Lukman Hakim
Universitas Mandiri Bina Prestasi Medan, Kota Medan, Provinsi Sumatera Utara, Indonesia.
Dikky Suryadi
Sekolah Tinggi Manajemen Informatika dan Komputer Al Muslim, Kabupaten Bekasi, Provinsi Jawa Barat, Indonesia.
-
Abdillah, A., Widianingsih, I., Buchari, R. A., & Nurasa, H. (2024). Big data security & individual (psychological) resilience: A review of social media risks and lessons learned from Indonesia. Array, 100336.
-
Ahmed, K., Khan, M. A., Haq, I., Al Mazroa, A., Syam, M. S., Innab, N., ... & Alkahtani, H. K. (2024). Social media’s dark secrets: A propagation, lexical and psycholinguistic oriented deep learning approach for fake news proliferation. Expert Systems with Applications, 255, 124650.
-
Anisa, D. F. N., Mukhlash, I., & Iqbal, M. (2023). Deteksi Berita Online Hoax Covid-19 Di Indonesia Menggunakan Metode Hybrid Long Short Term Memory dan Support Vector Machine. Jurnal Sains dan Seni ITS, 11(3), A101-A108.
-
Das, B. (2023). Multi-contextual learning in disinformation research: a review of challenges, approaches, and opportunities. Online Social Networks and Media, 34, 100247. https://doi.org/10.1016/j.osnem.2023.100247.
-
DickiPrabowo, R., Widaningrum, I., & Karaman, J. (2025). SISTEM DETEKSI BERITA HOAX PEMILU 2024 INDONESIA MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR (KNN) DAN SUPPORT VECTOR MACHINE (SVM). JIKO (Jurnal Informatika dan Komputer), 9(1), 93-111. http://dx.doi.org/10.26798/jiko.v9i1.1424.
-
Eldo, H., Ayuliana, A., Suryadi, D., Chrisnawati, G., & Judijanto, L. (2024). Penggunaan Algoritma Support Vector Machine (SVM) Untuk Deteksi Penipuan pada Transaksi Online. Jurnal Minfo Polgan, 13(2), 1627-1632. https://doi.org/10.33395/jmp.v13i2.14186.
-
Haq, M. Z., Octiva, C. S., Ayuliana, A., Nuryanto, U. W., & Suryadi, D. (2024). Algoritma Naïve Bayes untuk Mengidentifikasi Hoaks di Media Sosial. Jurnal Minfo Polgan, 13(1), 1079-1084. https://doi.org/10.33395/jmp.v13i1.13937.
-
Rahayu, S. P. D. (2021). Analisis Strategi Diskominfo Pamekasan Dalam Menangkal Konten Hoaks Covid-19 di Media Sosial (Edisi Maret-Desember 2020) (Doctoral dissertation, INSTITUT AGAMA ISLAM NEGERI MADURA).
-
Rangkuti, A. H., Ayuliana, A., & Fahri, M. (2018). Improving image classification using fuzzy neural network and backtracking algorithm. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-3), 123-128.
-
Salsabila, A. A., Dewi, D. A., & Hayat, R. S. (2024). Pentingnya literasi di era digital dalam menghadapi hoaks di media sosial. Inspirasi Dunia: Jurnal Riset Pendidikan Dan Bahasa, 3(1), 45-54. https://doi.org/10.58192/insdun.v3i1.1775.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.