Identifikasi Berita Hoax dengan Menerapkan Algoritma Text Mining
DOI:
https://doi.org/10.47065/jieee.v2i3.888Keywords:
Identification; Hoax; Text MiningAbstract
News is new information or information about something that is currently happening, presented in the form of print media, broadcasts, social media or word of mouth to third parties or multiple people. The spread of this news is now very much happening both from social media or messages. However, the problem is that no one can guarantee the truth of the news received. Text mining is the application of data mining concepts and techniques to look for patterns in text, the process of analyzing text to find useful information for a particular purpose. Another definition related to text mining is that text mining is data mining in the form of text where the data source is usually obtained from documents and the goal is to find words that can represent the contents of the document so that an analysis of the connectivity between documents can be carried out. To make it easier to identify news hoax requires a text mining algorithm, using a text mining algorithm can be useful to get real news. Based on the calculations of the TF-IDF algorithm, it can be concluded that the similarity of the meaning of the news whose truth is not yet known is closest to the news ofmerahputih.com with a confidence level of 23.11%.
Downloads
References
A. W. Fathurrahman, M. Thoriqulhaq, and F. Arianto, “Penerapan Machine Learning untuk Pengklasifikasian Hoaks pada Platform Media Sosial,” Senada, vol. 2022, no. Senada, pp. 66–68, 2022, [Online]. Available: https://senada.upnjatim.ac.id/index.php/senada/article/view/48%0Ahttps://senada.upnjatim.ac.id/index.php/senada/article/download/48/26.
N. I. Danu, “Identifikasi Berita Hoax Menggunakan Kombinasi Metode K-Nearest Neigbor (KNN) dan TF-IDF Berbasis Web Dengan Menggunakan Framework Codeigniter,” 2021, [Online]. Available: http://eprints.uwp.ac.id/id/eprint/3339/.
Y. T. Handika, S. Defit, and G. W. Nurcahyo, “Text Mining Dalam Membandingkan Metode Naïve Bayes Dengan C.45 Dalam Mengidentifikasi Berita Hoax Pada Media Sosial,” Rang Tek. J., vol. 5, no. 1, pp. 116–123, 2022, doi: 10.31869/rtj.v5i1.2855.
E. Edward, “Identifikasi Berita HOAX Berbasis Web Menggunakan Algoritma C4.5,” J. Ilmu Komput. dan Sist. Inf., vol. 9, no. 1, p. 53, 2021, doi: 10.24912/jiksi.v9i1.11558.
H. Ashari, D. Arifianto, H. Azizah, and A. Faruq, “Perbandingan Kinerja Algoritma Multinominal Naive Bayes (MNB, Multivariate Bernoulli dan Rocchio Algortihm Dalam Klasifikasi Konten Berita Hoax Berbahasa Indonesia Pada Media Sosial,” Http://Repository.Unmuhjember.Ac.Id, pp. 1–12, 2020.
R. K. Putri and M. Athoillah, “Identifikasi Berita Hoax Terkait Virus Corona Menggunakan Long Short-Term Memory,” Semin. Nas. Has. Ris. dan Pengabdi., no. April, pp. 506–513, 2022, [Online]. Available: https://snhrp.unipasby.ac.id/prosiding/index.php/snhrp/article/view/354/298.
I. A. Ropikoh, R. Abdulhakim, U. Enri, and N. Sulistiyowati, “Penerapan Algoritma Support Vector Machine (SVM) untuk Klasifikasi Berita Hoax Covid-19,” J. Appl. Informatics Comput., vol. 5, no. 1, pp. 64–73, 2021, doi: 10.30871/jaic.v5i1.3167.
N. Agustina and M. Hermawati, “Implementasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Berita Palsu pada Sosial Media,” Fakt. Exacta, vol. 14, no. 4, pp. 1979–276, 2021, doi: 10.30998/faktorexacta.v14i4.11259.
N. T. L. Toruan, “Sistem Pendukung Keputusan Pemilihan Pembawa Acara Berita Terbaik Menerapkan Metode OCRA,” Bull. Comput. Sci. Res., vol. 1, no. 3, pp. 71–78, 2021.
Y. T. Handika, S. Defit, and G. W. Nurcahyo, “TEXT MINING DALAM MEMBANDINGKAN METODE NAÏVE BAYES DENGAN C. 45 DALAM MENGIDENTIFIKASI BERITA HOAX PADA MEDIA SOSIAL,” Rang Tek. J., vol. 5, no. 1, pp. 116–123, 2022.
A. G. Tammam, S. Sucipto, and R. Indriati, “Hoax Detection at Social Media With Text Mining Clarification SystemBased,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 3, no. 2, pp. 94–100, 2018.
M. Z. Hossain, M. N. Akhtar, R. B. Ahmad, and M. Rahman, “A dynamic K-means clustering for data mining,” Indones. J. Electr. Eng. Comput. Sci., vol. 13, no. 2, pp. 521–526, 2019.
R. Wati, “Penerapan Algoritma Naive Bayes Dan Particle Swarm Optimization Untuk Klasifikasi Berita Hoax Pada Media Sosial,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 5, no. 2, pp. 159–164, 2020, doi: 10.33480/jitk.v5i2.1034.
M. Mashadi, AKUNTABILITAS IDEAL. 2018.
D. Maulina and R. Sagara, “Klasifikasi artikel hoax menggunakan support vector machine linear dengan pembobotan term frequency–Inverse document frequency,” J. Mantik Penusa, vol. 2, no. 1, 2018.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Identifikasi Berita Hoax dengan Menerapkan Algoritma Text Mining
ARTICLE HISTORY
Issue
Section
Copyright (c) 2023 Nitha Kumala Dewi

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).


