Indonesian Language Hoax News Classification Basedn on Naïve Bayes

Authors

  • Ari Sudrajat Politeknik TEDC Bandung
  • Ratna Rizky Wulandari Politeknik TEDC Bandung
  • Elvathna Syafwan Politeknik TEDC Bandung

DOI:

https://doi.org/10.33633/jais.v7i1.5985

Abstract

Hoax news in Indonesia causes various problems, therefore it is necessary to classify whether a news is in the hoax category or is valid. Naive Bayes is an algorithm that can perform classification but has a weakness, namely the selection of attributes that can affect accuracy so that it needs to be optimized by giving weights to attributes using the TF-IDF method. Classification using Naive Bayes and using TF-IDF as attribute weighting on a dataset of 600 data resulted in 82% accuracy, 84% precision, and 89% recall. The suggestion put forward is that it is better to use a larger number of datasets in order to produce higher accuracy.

References

B. Bhutani, N. Rastogi, P. Sehgal, and A. Purwar, “Fake News Detection Using Sentiment Analysis,” 2019 12th Int. Conf. Contemp. Comput. IC3 2019, pp. 1–5, 2019.

D. Katsaros, G. Stavropoulos, and D. Papakostas, “Which machine learning paradigm for fake news detection?,” Proc. - 2019 IEEE/WIC/ACM Int. Conf. Web Intell. WI 2019, pp. 383–387, 2019.

C. Juditha, “Interaksi Komunikasi Hoax di Media Sosial Serta Antisipasinya,” J. Pekommas, vol. 3, no. 1, pp. 31–34, 2018.

H. Mustofa and A. A. Mahfudh, “Klasifikasi Berita Hoax Dengan Menggunakan Metode Naive Bayes,” Walisongo J. Inf. Technol., vol. 1, no. 1, p. 1, 2019.

M. G. Hussain, M. Rashidul Hasan, M. Rahman, J. Protim, and S. Al Hasan, “Detection of Bangla Fake News using MNB and SVM Classifier,” Proc. - 2020 Int. Conf. Comput. Electron. Commun. Eng. iCCECE 2020, pp. 81–85, 2020.

M. Singh, M. Wasim Bhatt, H. S. Bedi, and U. Mishra, “Performance of bernoulli’s naive bayes classifier in the detection of fake news,” Mater. Today Proc., no. xxxx, 2020.

M. Granik and V. Mesyura, “Fake news detection using naive Bayes classifier,” 2017 IEEE 1st Ukr. Conf. Electr. Comput. Eng. UKRCON 2017 - Proc., pp. 900–903, 2017.

I. Y. R. Pratiwi, R. A. Asmara, and F. Rahutomo, “Study of hoax news detection using naïve bayes classifier in Indonesian language,” in 2017 11th International Conference on Information & Communication Technology and System (ICTS), 2017, pp. 73–78.

K. Poddar, G. B. D. Amali, and K. S. Umadevi, “Comparison of Various Machine Learning Models for Accurate Detection of Fake News,” 2019 Innov. Power Adv. Comput. Technol. i-PACT 2019, pp. 1–5, 2019.

E. Pudjiarti, “PREDIKSI SPAM EMAIL MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN PARTICLE SWARM OPTIMIZATION,” J. Pilar Nusa Mandiri, vol. XII, no. 2, pp. 171–181, 2016.

R. N. Devita, H. W. Herwanto, and A. P. Wibawa, “Perbandingan Kinerja Metode Naive Bayes dan K-Nearest Neighbor untuk Klasifikasi Artikel Berbahasa indonesia,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 4, p. 427, 2018.

Y. D. Pramudita, S. S. Putro, and N. Makhmud, “Klasifikasi Berita Olahraga Menggunakan Metode Naïve Bayes dengan Enhanced Confix Stripping Stemmer,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 3, p. 269, 2018.

H. Muhamad, C. A. Prasojo, N. A. Sugianto, L. Surtiningsih, and I. Cholissodin, “Optimasi Naïve Bayes Classifier Dengan Menggunakan Particle Swarm Optimization Pada Data Iris,” J. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 3, p. 180, 2017.

D. A. Muthia, “Analisis Sentimen Pada Review Restoran Dengan Teks Bahasa Indonesia Mengunakan Algoritma Naive Bayes,” J. Ilmu Pengetah. Dan Teknol. Komput., vol. 2, no. 2, pp. 39–45, 2017.

T. Herdiawan Apandi and C. Agus Sugianto, “Analisis Komparasi Machine Learning Pada Data Spam Sms,” TEDC, vol. 12, no. 1, p. 58, 2018.

C. A. Sugianto and T. H. Apandi, “Pengaruh Tokenisasi Kata N-Grams Spam SMS Menggunakan Support Vector Machine,” in CITISEE 2017, 2017, pp. 5–9.

T. H. Apandi and C. A. Sugianto, “Penyaringan Spam Short Message Service Menggunakan Support Vector Machine,” Semin. Nas. Teknol. Inf. dan Komun. Terap., pp. 111–116, 2015.

K. M. A. Hasan, M. S. Sabuj, and Z. Afrin, “Opinion mining using Naïve Bayes,” in 2015 IEEE International WIE Conference on Electrical and Computer Engineering, WIECON-ECE 2015, 2016, pp. 511–514.

Downloads

Published

2022-05-19