Cyberbullying Detection Modelling  at Twitter Social Networking

Ika Yunida Anggraini; Sucipto Sucipto; Rini Indriati

doi:10.30595/juita.v6i2.3350

Authors

Ika Yunida Anggraini Universitas Nusantara PGRI Kediri
Sucipto Sucipto Universitas Nusantara PGRI Kediri
Rini Indriati Universitas Nusantara PGRI Kediri

DOI:

https://doi.org/10.30595/juita.v6i2.3350

Keywords:

Modelling, Cyberbullying, Twitter.

Abstract

Cybercrimes often happened in social networking sites. Cyber-bullying is a form of cybercrime that recently trended in one of popular social networking sites, Twitter. The practice of cyber-bullying on teenager can cause depression, murderer or suicidal thoughts and it needs a preventing action so it will not harmful to the victim. To prevent cyber-bullying a text mining modelling can be done to classify tweets on Twitter into two classes, bullying class and not bullying class. On this research we use NaÃ¯ve Bayes Classifier with five stages of pre-processing : replace tokens, transform case, tokenization, filter stopwords and n-grams. The validation process on this research used 10-Fold Cross Validation. To evaluate the performance of the model a Confusion Matrix table is used. The model on 10-Fold Cross Validation phase works well with 77,88% of precision , 94,75% of recall and 82,50% of accuracy with +/-5,12%Â of standard deviation.

References

[1] www.kominfo.go.id, “Kominfo : Pengguna Internet di Indonesia 63 Juta Orang,” 2017. [Daring]. Tersedia pada: http://kominfo.go.id/index.php/content/detail/3415/Kominfo+%3A+Pengguna+Internet+di+Indonesia+63+Juta+Orang/0/berita_satker#.VR_pF2OY6tE. [Diakses: 01-Jul-2018].

[2] H. Sanchez dan K. Shreyas, “Twitter Bullying Detection,” California, 2011.

[3] T. Viva, “1 dari 4 Remaja Pernah Alami Perundungan di Dunia Maya,” Gaya Hidup VIVA, 2017. [Daring]. Tersedia pada: https://www.viva.co.id/gaya-hidup/parenting/963329-1-dari-4-remaja-pernah-alami-perundungan-di-dunia-maya. [Diakses: 01-Jul-2018].

[4] J. W. Patchin dan S. Hinduja, Words Wound : Delete Cyberbullying and Make Kindness Go Viral. Minneapolis: Free Spirit Publishing, 2014.

[5] S. Hinduja dan J. W. Patchin, “Cyberbullying and Suicide,” 2010.

[6] H. Margono, X. Yi, dan G. K. Raikundalia, “Mining Indonesian Cyber Bullying Patterns in Social Networks,” in Proceedings of Thirty-Seventh Australasian Computer Science Conference (ACSC 2014), 2014, no. ACSC, hal. 115–124.

[7] J. E. Sembodo, E. B. Setiawan, dan Z. K. A. Baizal, “Data Crawling Otomatis pada Twitter,” in IND. SYMPOSIUM ON COMPUTING, 2016, no. September, hal. 11–16.

[8] RapidMiner, “Replace ( Dictionary ),” 2018. [Daring]. Tersedia pada: https://docs.rapidminer.com/latest/studio/operators/blending/values/replace_dictionary.html. [Diakses: 01-Jul-2018].

[9] A. Go, R. Bhayani, dan L. Huang, “Twitter Sentiment Classification using Distant Supervision,” USA, 2009.

[10] B. Liu, Web Data Mining : Exploring Hyperlinks, Contents, and Usage Data, Second Edi. Berlin: Springer, 2011.

[11] J. Han dan M. Kamber, Data Mining : Concepts and Techniques, 2nd Editio. San Francisco: Morgan Kaufmann Publishers, 2006.

[12] S. Genzer, “Term Frequencies and TF-IDF: How are these calculated?,” RapidMiner Knowledge Base, 2018. [Daring]. Tersedia pada: community.rapidminer.com/t5/RapidMiner-Text-Analytics-Web/Term-Frequencies-and-TF-IDF-How-are-these-calculated/ta-p/46333. [Diakses: 01-Jul-2018].

[13] D. Jurafsky dan J. H. Martin, Speech and Language Processing. New Jersey: Prentice Hall, 2009.

[14] F. Gorunescu, Data Mining : Concepts, Models and Techniques. Berlin: Springer, 2011.

[15] Sucipto, Kusrini, and E. L. Taufiq, “Classification method of multi-class on C4.5 algorithm for fish diseases,” in Proceeding - 2016 2nd International Conference on Science in Information Technology, ICSITech 2016: Information Science for Green Society and Environment, 2016, pp. 5–9.

[16] S. Sucipto, “Analisa Hasil Rekomendasi Pembimbing Menggunakan Multi-Attribute Dengan Metode Weighted Product,” Fountain Informatics J., vol. 2, no. 1, p. 27, May 2017.