Klasifikasi Ujaran Kebencian pada Media Sosial Twitter Menggunakan Support Vector Machine

Hate Speech Classification on Twitter Using Support Vector Machine

  • Oryza Habibie Rahman Universitas Jenderal Achmad Yani
  • Gunawan Abdillah Universitas Jenderal Achmad Yani
  • Agus Komarudin Universitas Jenderal Achmad Yani
DOI: https://doi.org/10.29207/resti.v5i1.2700
Keywords: classification, support vector machine, hate speech, twitter, kernel

Abstract

Nowadays social media has become a place for peoples to express their opinions, there are many ways that can be done to express both positive and negative opinions. Hate speech is one of the problems that we find quite a lot in cyberspace, that things can be detrimental to many parties. Twitter as one of social media, can be used as a source of analysis about people's behavior in cyberspace. Many of our society that unconsciously act of hate speech on social media, therefore this study finds out how people's behavior patterns in cyberspace and the main issue of hate speech on a particular topic and time period by classify it into five classes, namely ethnicity, religion, race, inter-groups and neutral using Support Vector Machine. In this study also compares three kernel that common to use and the result is the system can classify hate speech by using RBF kernel and got the highest result with 93% accuracy on 700 data train and 300 data test.

Downloads

Download data is not yet available.

References

H. Nurrahmi and D. Nurjanah, “Indonesian Twitter Cyberbullying Detection using Text Classification and User Credibility,” 2018 International Conference on Information and Communications Technology, ICOIACT 2018, pp. 543–548, 2018.

A. M. Pravina, I. Cholissodin, and P. P. Adikara, “Analisis Sentimen Tentang Opini Maskapai Penerbangan pada Dokumen Twitter Menggunakan Algoritme Support Vector Machine ( SVM ),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, vol. 3, no. 3, pp. 2789–2797, 2019.

E. E. Pratama and B. R. Trilaksono, “Klasifikasi Topik Keluhan Pelanggan Berdasarkan Tweet dengan Menggunakan Penggabungan Feature Hasil Ekstraksi pada Metode Support Vector Machine (SVM),” Jurnal Edukasi dan Penelitian Informatika (JEPIN), vol. 1, no. 2, pp. 53–59, 2015.

I. Sunni and D. H. Widyantoro, “Analisis Sentimen dan Ekstraksi Topik Penentu Sentimen pada Opini Terhadap Tokoh Publik,” Jurnal Sarjana Institut Teknologi Bandung Bidang Teknik Elektro dan Informatika, vol. 1, no. 2, pp. 200–206, 2012.

G. Vinodhini and R. Chandrasekaran, “Sentiment Analysis and Opinion Mining: A Survey,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 2, no. 6, pp. 282–292, 2012.

I. Afina, R. Mulia, M. I. Fanany, and Y. Ekanata, “Hate Speech Detection in the Indonesian Language: A Dataset and Preliminary Study,” 2017 International Conference on Advanced Computer Science and Information Systems(ICACSIS), pp. 233–237, 2017.

V. Korde and C. N. Mahender, “Text Classification and Classifiers:A Survey,” International Journal of Artificial Intelligence & Applications, vol. 3, no. 2, pp. 85–99, 2012.

S. Wang and C. D. Manning, “Baselines and bigrams: Simple, good sentiment and topic classification,” 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference, vol. 2, no. 1, pp. 90–94, 2012.

Z. Liu, X. Lv, K. Liu, and S. Shi, “Study on SVM compared with the other text classification methods,” 2nd International Workshop on Education Technology and Computer Science, ETCS 2010, vol. 1, pp. 219–222, 2010.

W. A. Luqyana, I. Cholissodin, and R. S. Perdana, “Analisis Sentimen Cyberbullying Pada Komentar Instagram dengan Metode Klasifikasi Support Vector Machine,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, vol. 2, no. 11, pp. 4704–4713, 2018.

M. K. Dalal and M. A. Zaveri, “Automatic Text Classification: A Technical Review,” International Journal of Computer Applications, vol. 28, no. 2, pp. 37–40, 2011.

S. Fatima, “Text Document Categorization using Support Vector Machine,” International Research Journal of Engineering and Technology (IRJET), vol. 4, no. 2, pp. 141–147, 2017.

S. K. Lidya, O. S. Sitompul, and S. Efendi, “Sentiment Analysis Pada Teks Bahasa Indonesia Menggunakan Support Vector Machine ( Svm ),” Seminar Nasional Teknologi dan Komunikasi 2015, no. Sentika, pp. 1–8, 2015.

Y. Ahuja and S. Kumar Yadav, “Multiclass Classification and Support Vector Machine,” Global Journal of Computer Science and Technology Interdisciplinary, vol. 12, no. 11, pp. 14–19, 2012.

R. Moraes, J. F. Valiati, and W. P. Gavião Neto, “Document-level sentiment classification: An empirical comparison between SVM and ANN,” Expert Systems with Applications, vol. 40, no. 2, pp. 621–633, 2013.

Published
2021-02-20
How to Cite
Oryza Habibie Rahman, Gunawan Abdillah, & Agus Komarudin. (2021). Klasifikasi Ujaran Kebencian pada Media Sosial Twitter Menggunakan Support Vector Machine. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(1), 17 - 23. https://doi.org/10.29207/resti.v5i1.2700
Issue
Vol 5 No 1 (2021): Februari 2021
Section
Information Technology Articles

Copyright (c) 2021 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright in each article belongs to the author

  1. The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
  2. Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;