Comparison Support Vector Machine and Naive Bayes Methods for Classifying Cyberbullying in Twitter

Chamidah, Nur and Sahawaly, Reiza (2021) Comparison Support Vector Machine and Naive Bayes Methods for Classifying Cyberbullying in Twitter. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, 7 (2). pp. 338-346.

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Abstract

Twitter users in Indonesia in 2019 were recorded at 6.43 million. The high level of Twitter users makes it allows for free opinion to anyone, it can cause cyberbullying. Victims of cyberbullying experienced higher levels of depression than other verbal acts of violence. The forms of cyberbullying that occurs on Twitter are Flamming, Denigration, and Body Shaming. The research contribution is able to make social media developers and users more aware of the type of cyberbullying that social media users sometimes do without realizing it. Social media developers can prevent cyberbullying by using policies such as word detection and filtering features that indicate cyberbullying more accurately by classifying it by type and using the most accurate method. To classify cyberbullying forms in twitter, in this study we use the Naïve Bayes method and Support Vector Machine (SVM) and compare them based on classification accuracy. This research will also identify words that are characteristic of each category of cyberbullying so that each category is easy to identify by social media users and makes it easier to avoid cyberbullying. The results of this study are the classification accuracy of Naïve Bayes of 97.99% and the classification accuracy of SVM of 99.60%. It means that SVM is better than Naïve Bayes for classifying the forms of cyberbullying in Twitter.

Item Type: Artikel Umum
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisi / Prodi: Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Electrical Engineering (S1-Teknik Elektro)
Depositing User: M.Eng. Alfian Ma'arif
Date Deposited: 14 Oct 2021 02:40
Last Modified: 14 Oct 2021 02:40
URI: http://eprints.uad.ac.id/id/eprint/28995

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