N-gram and Kernel Performance Using Support Vector Machine Algorithm for Fake News Detection System


Deny Jollyta(1); Gusrianty Gusrianty(2); Prihandoko Prihandoko(3*); Darmanta Sukrianto(4);

(1) Institut Bisnis dan Teknologi Pelita Indonesia
(2) Institut Bisnis dan Teknologi Pelita Indonesia
(3) Universitas Gunadarma
(4) AMIK Mahaputera
(*) Corresponding Author

  

Abstract


The modern technological advancements have made it simpler for fake news to circulate online. The researchers have developed several strategies to overcome this obstacle, including text classification, distribution network analysis, and human-machine hybrid methods. The most common method is text categorization, and many researchers offer deep learning and machine learning models as remedies. An Indonesian language fake news detection system based on news headlines was developed in this work using the Support Vector Machine (SVM) kernel and n-gram. The objective of this research is to identify the model that produces the best performance outcomes. The system deployment on the web will employ the model that produces the greatest outcomes. According to the research findings, the linear kernel SVM algorithm produces the best results, with an accuracy value of 0.974. Furthermore, the bigram feature used in the development of a classification model does not increase the precision of fake news identification in Indonesian. Utilizing the unigram function yields the most accurate results.

Keywords


Fake News; Kernel; N-gram; Support Vector Machine; Text Classification

  
  

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doi  https://doi.org/10.33096/ilkom.v15i3.1770.398-404 		  

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J. P. Baptista, A. Gradim, and E. Correia, “The relationship between the belief in fake news and the strategies to seek information from young Portuguese people,” Observatorio, vol. 16, no. 3, pp. 203–232, 2022, doi: 10.15847/obsOBS16320222082.

M. Karami, T. H. Nazer, and H. Liu, “Profiling Fake News Spreaders on Social Media through Psychological and Motivational Factors,” 2021. doi: 10.1145/3465336.3475097.

P. Nordberg, J. Kavrestad, and M. Nohlberg, “Automatic detection of fake news,” in 6th International Workshop on Socio-Technical Perspective in IS Development (STPIS’20), 2020, pp. 168–179.

M. Albahar and J. Almalki, “Deepfakes: Threats and countermeasures systematic review,” J. Theor. Appl. Inf. Technol., vol. 97, no. 22, pp. 3242–3250, 2019.

R. R. Sani, Y. A. Pratiwi, S. Winarno, E. D. Udayanti, and F. Alzami, “Analisis Perbandingan Algoritma Naive Bayes Classifier dan Support Vector Machine untuk Klasifikasi Berita Hoax pada Berita Online Indonesia,” J. Masy. Inform., vol. 13, no. 2, pp. 85–98, 2022, doi: 10.14710/jmasif.13.2.47983.

D. Jollyta, G. Gusrianty, and D. Sukrianto, “Analysis of Slow Moving Goods Classification Technique: Random Forest and Naïve Bayes,” Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 5, no. 2, pp. 134–139, 2019, doi: 10.23917/khif.v5i2.8263.

B. Probierz, P. Stefanski, and J. Kozak, “Rapid detection of fake news based on machine learning methods,” Procedia Comput. Sci., vol. 192, no. January, pp. 2893–2902, 2021, doi: 10.1016/j.procs.2021.09.060.

T. Chauhan and H. Palivela, “Optimization and improvement of fake news detection using deep learning approaches for societal benefit,” Int. J. Inf. Manag. Data Insights, vol. 1, no. 2, p. 11, 2021, doi: 10.1016/j.jjimei.2021.100051.

Y. HaCohen-Kerner, D. Miller, and Y. Yigal, “The influence of preprocessing on text classification using a bag-of-words representation,” PLoS One, vol. 15, no. 5, pp. 1–22, 2020, doi: 10.1371/journal.pone.0232525.

J. Y. Khan, M. T. I. Khondaker, S. Afroz, G. Uddin, and A. Iqbal, “A benchmark study of machine learning models for online fake news detection,” Mach. Learn. with Appl., vol. 4, no. March, p. 12, 2021, doi: 10.1016/j.mlwa.2021.100032.

V. Kumar, A. Kumar, A. K. Singh, and A. Pachauri, “Fake News Detection using Machine Learning and Natural Language Processing,” Int. J. Recent Technol. Eng., vol. 7, no. 6, pp. 844–847, 2019, doi: 10.1109/ICTAI53825.2021.9673378.

F. S. Jumeilah, “Penerapan Support Vector Machine (SVM) untuk Pengkategorian Penelitian,” Resti, vol. 1, no. 1, pp. 19–25, 2017.

R. Rahmaddeni, M. K. Anam, Y. Irawan, S. Susanti, and M. Jamaris, “Comparison of Support Vector Machine and XGBSVM in Analyzing Public Opinion on Covid-19 Vaccination,” Ilk. J. Ilm., vol. 14, no. 1, pp. 32–38, 2022, doi: 10.33096/ilkom.v14i1.1090.32-38.

A. E. Budiman and A. Widjaja, “Analisis Pengaruh Teks Preprocessing Terhadap Deteksi Plagiarisme Pada Dokumen Tugas Akhir,” J. Tek. Inform. dan Sist. Inf., vol. 6, no. 3, pp. 475–488, 2020, doi: 10.28932/jutisi.v6i3.2892.

L. Agusta, “Perbandingan Algoritma Stemming Porter Dengan Algoritma Nazief & Adriani Untuk Stemming Dokumen Teks Bahasa Indonesia,” in Konferensi Nasional Sistem dan Informatika 2009, 2009, no. KNS&I09-036, pp. 196–201.

P. Kanani and M. Padole, “Deep learning to detect skin cancer using google colab,” Int. J. Eng. Adv. Technol., vol. 8, no. 6, pp. 2176–2183, 2019, doi: 10.35940/ijeat.F8587.088619.

M. A. Rosid, A. S. Fitrani, I. R. I. Astutik, N. I. Mulloh, and H. A. Gozali, “Improving Text Preprocessing for Student Complaint Document Classification Using Sastrawi,” in IOP Conference Series: Materials Science and Engineering, 2020, vol. 874, no. 1, pp. 1–7, doi: 10.1088/1757-899X/874/1/012017.


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