An Indonesian Hoax News Detection System Using Reader Feedback and Naïve Bayes Algorithm

1. Hornby, A. S. Oxford Advanced Learner’s Dictionary of Current English. Oxford University Press, UK, 1995.Search in Google Scholar

2. Vukovic, M., K. Pripuzic, H. Belani. An Intelligent Automatic Hoax Detection System. – Knowledge-Based and Intelligent Information and Engineering Systems, 2009, pp. 318-325.10.1007/978-3-642-04595-0_39Search in Google Scholar

3. Petkovic, T., Z. Kostanjcar, P. Pale. e-Mail System for Automatic Hoax Recognition. – In: 27th MIPRO International Conference, 2005, pp. 117-121.Search in Google Scholar

4. Anonymous. Hoax Email Captures Yahoo’s Customer Credit Numbers, 2002, p. 3.10.1016/S1361-3723(02)01104-1Search in Google Scholar

5. Almeida, T. A., T. P. Silva, I. Santos, J. M. G. Hidalgo. Text Normalization and Semantic Indexing to Enhance Instant Messaging and SMS Spam Filtering. – Knowledge-Based System Journal, Vol. 108, 2016, pp. 25-32.10.1016/j.knosys.2016.05.001Search in Google Scholar

6. Purnomo, M. H., S. Sumpeno, E. I. Setiawan, D. Purwitasari. Biomedical Engineering Research in the Social Network Analysis Era : Stance Classification for Analysis of Hoax Medical News in Social Media. – In: 2nd International Conference on Computer Science and Computational Intelligence, Bali, 2017.Search in Google Scholar

7. Cunningham, E., W. Marcason. Internet Hoaxes: How to Spot Them and How to Debunk Them. – Journal of the American Dietetic Association, Vol. 101, 2001, No 4, p. 460.10.1016/S0002-8223(01)00117-1Search in Google Scholar

8. Ishak, A., Y. Y. Chen, S.-P. Yong. Distance-Based Hoax Detection System. – In: International Conference on Computer & Information Science (ICCIS’12), Kuala Lumpur, Malaysia, 2012, pp. 215-220.10.1109/ICCISci.2012.6297242Search in Google Scholar

9. Chen, Y. Y., S.-P. Yong, A. Ishak. Email Hoax Detection System Using Levenshtein Distance Method. – Journal of Computers, Vol. 9, 2014, No 2, pp. 441-446.10.4304/jcp.9.2.441-446Search in Google Scholar

10. Rasywir, E., A. Purwarianti. Eksperimen Pada Sistem Klasifikasi Berita Hoax Berbahasa Indonesia Berbasis Pembelajaran Mesin. – Jurnal Cybermatika, Vol. 3, 2015, No 2, pp. 1-8.Search in Google Scholar

11. Prasetijo, A. B., R. R. Isnanto, D. Eridani, Y. A. A. Soetrisno, M. Arfan, A. Sofwan. Hoax Detection System on Indonesian News Sites Based on Text Classification Using SVM and SGD. – In: 4th International Conference on Information Technology, Computer, and Electrical Engineering, Semarang, Indonesia, 2017, pp. 45-49.10.1109/ICITACEE.2017.8257673Search in Google Scholar

12. Pratiwi, I. Y. R., R. A. Asmara, F. Rahutomo. Study of Hoax News Detection Using Naive Bayes Classifier in Indonesian Language. – In: International Conference on Information & Communication Technology and System, 2017, pp. 74-78.10.1109/ICTS.2017.8265649Search in Google Scholar

13. Sirajudeen, S. M., N. F. A. Azmi, A. I. Abubakar. Online Fake News Detection Algorithm. – Journal of Theoretical and Applied Information Technology (JATIT), Vol. 95, 2017, No 17, pp. 4114-4122.Search in Google Scholar

14. Hernandez, J., C., Hernandez, C. J., Sierra, J. M., Ribagorda. A First Step towards Automatic Hoax Detection. – In: Proc. of 36th Annual 2002, Atlantic City, NJ, USA, 2002, pp. 102-114.Search in Google Scholar

15. Kim, H., J. Kim, J. Kim, P. Lim. Towards Perfect Text Classification with Wikipedia-Based Semantic Naïve Bayes Learning. – Neurocomputing, Vol. 315, November 2018, pp. 128-134.10.1016/j.neucom.2018.07.002Search in Google Scholar

16. Rasjid, Z. E., R. Setiawan. Performance Comparison and Optimization of Text Document Classification Using k-NN and Naive Bayes Classification Techniques. – Procedia Computer Science, Vol. 116, Bali, Indonesia, 2017, pp. 107-112.10.1016/j.procs.2017.10.017Search in Google Scholar

17. Granik, M., V. Mesyura. Fake News Detection Using Naive Bayes Classifier. – In: IEEE First Ukraine Conference on Electrical and Computer Engineering, Kiev, Ukraine, 2017, pp. 900-903.10.1109/UKRCON.2017.8100379Search in Google Scholar

18. Patil, D. R., J. B. Patil. Malicious URLs Detection Using Decision Tree Classifiers and Majority Voting Technique. – Cybernetics and Information Technologies, Vol. 18, 2018, No 1, pp. 11-29.10.2478/cait-2018-0002Search in Google Scholar

19. Fatmawati, T., B. Zaman, I. Werdiningsih. Implementation of the Common Phrase Index Method on the Phrase Query for Information Retrieval. – In: Proc. of International Conference on Mathematics: Pure, Applied and Computation, AIP Conference, 2017.Search in Google Scholar

20. Christopher, M., P. Raghavan, H. Schütze. An Introduction to Information Retrieval. – Natural Language Engineering, Vol. 16, 2010, No 1, pp. 100-103.10.1017/S1351324909005129Search in Google Scholar

21. Kallimani, J. S., K. G. Srinivasa, E. B. Reddy. Summarizing News Paper Articles: Experiments with Ontology-Based Customized, Extractive Text Summary and Word Scoring. – Cybernetics and Information Technologies, Vol. 12, 2012, No 2, pp. 35-50.10.2478/cait-2012-0011Search in Google Scholar

22. Tala, F. Z. A Study of Stemming Effects on Information Retrieval in Bahasa Indonesia. Thesis, Universiteit van Amsterdam, Netherlands, 2003.Search in Google Scholar

23. Agusta, L. Perbandingan Algoritma Stemming Porter Dengan Algoritma Nazief & Adriani Untuk Stemming Dokumen Teks Bahasa Indonesia. – In: Konferensi Nasional Sistem dan Informatika, Bali, Indonesia, 2009, pp. 196-201.Search in Google Scholar

24. Li, B., L. Han. Distance Weighted Cosine Similarity Measure for Text Classification. – In: International Conference on Intelligent Data Engineering and Automated Learning, Berlin, Heidelberg, 2013, pp. 611-618.10.1007/978-3-642-41278-3_74Search in Google Scholar

25. Al-Anzi, F. S., D. AbuZeina. Toward an Enhanced Arabic Text Classification Using Cosine Similarity and Latent Semantic Indexing. – Journal of King Saud University – Computer and Information Sciences, Vol. 29, April 2016, pp. 189-195.10.1016/j.jksuci.2016.04.001Search in Google Scholar

26. Theodoridis, S., K. Koutroumbas. Pattern Recognition: Second Recognition. Academic Press, 2003.Search in Google Scholar

27. Tung, K. T., N. D. Hung, L. T. My Hanh. A Comparison of Algorithms Used to Measure the Similarity between Two Documents. – International Journal of Advanced Research in Computing Engineering and Technology (IJARCET), Vol. 4, 2015, No 4, pp. 1117-1121.Search in Google Scholar

28. Han, J., M. Kamber, J. Pei. Data Mining : Concepts and Techniques. – In: The Morgan Kaufmann Series in Data Management Systems, 2011, pp. 83-124.Search in Google Scholar

29. Bermudez-Ortega, J., E. Besada-Portas, J. A. Lopez-Orozco, J. A. Bonache-Seco, J. M. de la Cruz. Remote Web-Based Control Laboratory for Mobile Devices Based on EJsS, Raspberry Pi and Node.js. – Elsevier, Ltd., Vol. 48, 2015, No 29, pp. 158-163.10.1016/j.ifacol.2015.11.230Search in Google Scholar

30. Pasquali, S., K. Faaborg. Mastering Node.js: Build Robust and Scalable Real-Time Server-Side. Web Applications Efficiently, Vol. 2. Birmingham, Mumbai, Packt Publishing, 2017.Search in Google Scholar

31. Chodorow, K. MongoDB: The Definitive Guide Powerful and Scalable Data Storage. O’Reilly Media, Inc., 2013.Search in Google Scholar

32. Priandini, N., B. Zaman, E. Purwanti. Categorizing Document by Fuzzy c-Means and k-Nearest Neighbors’ Approach. – In: AIP Conference Proceedings, 2012.Search in Google Scholar

33. Zaman, B., E. Winarko. Analisis Fitur Kalimat untuk Peringkas Teks Otomatis pada Bahasa Indonesia. – International Journal of Computing and Cybernetics System, Vol. 5, 2011, No 2, pp. 60-68.10.22146/ijccs.2019Search in Google Scholar