Naïve Bayes’s Experiment On Hoax News Detection In Indonesian Language
Article Sidebar
Read Counter : 3928
Download : 6227
Main Article Content
Abstract
Website and blog are popular as a media to spread news. The validity of an article of news’s can either be valid or fake. A fake article of news is usually called a hoax news article. The purpose of making hoax news is to persuade, manipulate, affect to people to do something that contradicts or prevents the right action. A hoax news usually used threats or misleading information to make them believe things that are not real. This research proposes an experiment using naïve Bayes to detect hoax news in Bahasa Indonesia. In this research, we use our own dataset consisting of a total of 600 valid and hoax articles. We asked three reviewers to conduct manual classification for our dataset. Final tagging was obtained by adopting the maximum score from the three reviewers. In our experiment, we show that naïve Bayes can classify Indonesian online news articles with term frequency feature using the PHP-ML library component’s. We obtained an accuracy is 82.6% with static testing and 68.33% with dynamic testing. We give free access to the dataset so the future research can replicate, comparing the result and make a baseline testing.
Keywords : Hoax News Detection, Naïve Bayes Classifier.
Article Details
Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. 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 acknowledgement of its initial publication in this journal.
3. 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 (See The Effect of Open Access).
References
Afroz, Sadia, Michael Brennan, and Rachel Greenstadt. "Detecting hoaxes, frauds, and deception in writing style online." Security and Privacy (SP), 2012 IEEE Symposium on. IEEE, 2012.
Banerjee, Snehasish, Alton YK Chua, and Jung-Jae Kim. "Using supervised learning to classify authentic and fake online reviews." Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication. ACM, 2015.
D. Manning, Christopher; Raghavan, Prabhakar; Schutze, Hinrich. “An Introduction to Information Retrieval”. Cambridge, England : Cambridge University Press. 2009
Djuraid. Husnun N. “Panduan Menulis Berita”. Malang : UPT. Penerbitan Universitas Muhammadiyah Malang. 2006
Fitri Sari, Riri, Adi Wicaksana, Burhan. “Teknik Ekstraksi Informasi di Web”. Yogyakarta : Andi. 2011
Hernandez, Julio César, et al. "A first step towards automatic hoax detection." Security Technology, 2002. Proceedings. 36th Annual 2002 International Carnahan Conference on. IEEE, 2002.
Ishak, Adzlan, Y. Y. Chen, and Suet-Peng Yong. "Distance-based hoax detection system." Computer & Information Science (ICCIS), 2012 International Conference on. Vol. 1. IEEE, 2012.
Iskandar Muda, Deddy. “Jurnalistik Televisi Menjadi Reporter Profesional”. Bandung : Remaja Rosdakarya. 2005
Ishwara, Luwi. “Catatan – Catatan Jurnalisme Dasar”. Jakarta : Buku Kompas. 2005
Muzad, Aad Miqdad Muadz, and Faisal Rahutomo. "Korpus Berita Daring Bahasa Indonesia Dengan Depth First Focused Crawling." Prosiding Sentrinov (Seminar Nasional Terapan Riset Inovatif). Vol. 2. No. 1. 2016.
Pratiwi, Inggrid Yanuar Risca, Rosa Andrie Asmara, and Faisal Rahutomo. "Study of hoax news detection using naïve bayes classifier in Indonesian language." Information & Communication Technology and System (ICTS), 2017 11th International Conference on. IEEE, 2017.
Rasywir, Errissya, and Ayu Purwarianti. "Eksperimen pada Sistem Klasifikasi Berita Hoax Berbahasa Indonesia Berbasis Pembelajaran Mesin." Jurnal Cybermatika 3.2 (2016).
Rubin, Victoria L., Yimin Chen, and Niall J. Conroy. "Deception detection for news: three types of fakes." Proceedings of the 78th ASIS&T Annual Meeting: Information Science with Impact: Research in and for the Community. American Society for Information Science, 2015.
Tacchini, Eugenio, et al. "Some like it hoax: Automated fake news detection in social networks." arXiv preprint arXiv:1704.07506 (2017).
Thabtah, Fadi, et al. "Naïve Bayesian based on Chi Square to categorize Arabic data." proceedings of The 11th International Business Information Management Association Conference (IBIMA) Conference on Innovation and Knowledge Management in Twin Track Economies, Cairo, Egypt. 2009.
Vuković, Marin, Krešimir Pripužić, and Hrvoje Belani. "An intelligent automatic hoax detection system." International Conference on Knowledge-Based and Intelligent Information and Engineering Systems. Springer, Berlin, Heidelberg, 2009.