Studi Algoritma Linear Support Vector Machine pada Deteksi Ujaran Kebencian Berbahasa Indonesia

Indonesian

  • Alfi Ramdhani Universitas Muhammadiyah Prof.DR.HAMKA

Abstract

Ekspresi ujaran kebencian merupakan suata fenomena yang berkembangan di dunia masyarakat era modern ini, banyak dari pengguna media sosial memanfaatkannya untuk mengekspresikan perasaan mereka maupun kehidupannya.  Namun dari fenomena ini semua berdampak kepada  lingkungan masyarakat yang terkesan sangat bebas mengekspresikan ujaran kebencian  dan berujung kepada tindakan kejahatan, entah darimana asal-usul penyebab terjadinya, bisa jadi karena pengaruh provokasi atau hal-hal lainnya yang persuasif. Maka dari itu tujuan penelitian melakukan studi terhadap algoritma Linear Support Vector Machine dalam melakukan deteksi ujaran kebencian berbahasa Indonesia. Metode yang digunakan adalah algoritma Linear Support Vector Machine dengan feature Word N Gram. Dari hasil percobaan, diperoleh hasil evaluasi akurasi sebesar 86.55 % dengan metode 10-fold cross validation
 

Author Biography

Alfi Ramdhani, Universitas Muhammadiyah Prof.DR.HAMKA
Program Studi Informatika Fakultas Teknik

References

W. Warner and J. Hirschberg, “Detecting hate speech on the world wide web,” Proceeding LSM ’12 Proc. Second Work. Lang. Soc. Media, no. Lsm, pp. 19–26, 2012.

Z. Waseem and D. Hovy, “Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter,” Proc. NAACL Student Res. Work., pp. 88–93, 2016.

I. Alfina, R. Mulia, M. I. Fanany, and Y. Ekanata, “A Dataset and Preliminary Study,” Adv. Comput. Sci. Inf. Syst. (ICACSIS), 2017 Int. Conf. 2017, pp. 1–5, 2017.

T. Davidson, D. Warmsley, M. Macy, and I. Weber, “Automated Hate Speech Detection and the Problem of Offensive Language,” Proc. 11th Int. AAAI Conf. Web Soc. Media, no. Icwsm, pp. 512–515, 2017.

B. Gambäck and U. K. Sikdar, “Using Convolutional Neural Networks to Classify Hate-Speech,” Proc. First Work. Abus. Lang. Online, no. 7491, pp. 85–90, 2017.

P. Badjatiya, S. Gupta, M. Gupta, and V. Varma, “Deep Learning for Hate Speech Detection in Tweets,” vol. 2017, no. April, pp. 1–3, 2017.

G. Chowdhury, “Natural language processing,” Annu. Rev. Inf. Sci. Technol., vol. 37, pp. 51–83, 2003.

N. Kumar, “A Review on Machine Learning Algorithms , Tasks and Applications,” Int. J. Adv. Res. Comput. Eng. Technol., vol. 6, no. 10, 2017.

A. S. Nugroho, A. B. Witarto, and D. Handoko, “Support vector machine: Teori dan Aplikasinya dalam Bioinformatika,” IlmuKomputer.Com., 2003.

C. Cortes and V. Vapnik, “Support-Vector Networks,” Mach. Learn., vol. 297, no. 20, pp. 273–297, 1995.

D. Fradkin and I. Muchnik, “Support Vector Machines for Classification,” DIMACS Ser. Discret. Math. Theor. Comput. Sci., pp. 1–9, 2000.

Y. Tang, “Deep Learning using Linear Support Vector Machines,” ICML 2013 Challenges Represent. Learn. Work., 2013.

F. Pedregosa, R. Weiss, and M. Brucher, “Scikit-learn : Machine Learning in Python,” J. ofMachine Learn. Res., vol. 12, pp. 2825–2830, 2011.

T. Davidson, D. Warmsley, M. Macy, and I. Weber, “Automated Hate Speech Detection and the Problem of Offensive Language ∗,” 2013.
Published
2019-01-10
How to Cite
RAMDHANI, Alfi. Studi Algoritma Linear Support Vector Machine pada Deteksi Ujaran Kebencian Berbahasa Indonesia. Prosiding Seminar Nasional Teknoka, [S.l.], v. 3, p. I42-I44, jan. 2019. ISSN 2580-6408. Available at: <https://journal.uhamka.ac.id/index.php/teknoka/article/view/2899>. Date accessed: 18 mar. 2019. doi: https://doi.org/10.22236/teknoka.v3i0.2899.

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.