Hate Speech Detection On Indonesia Language Twitter Using The Naive Bayes Algorithm

Taufik Putri, Nadia Zerlinda and , Endang Wahyu Pamungkas, S.Kom, M.Kom. (2023) Hate Speech Detection On Indonesia Language Twitter Using The Naive Bayes Algorithm. Skripsi thesis, Universitas Muhammadiyah Surakarta.

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Abstract

Social media is a medium for disseminating information online and is very easy to do because there are no rules for writing in online media. Therefore, anyone with access to social media can disseminate information without prior filtering, which can lead to hate speech. Hate speech is any form of communication in the form of harsh words that provoke both groups and individuals. However, these harsh words often cause hate speech to be banned in public spaces such as social media. The social media used in this study is Twitter, because data can be retrieved through the Twitter library. This study aims to create a tool to detect hate speech on Instagram comments in Indonesian. This study uses the Naive Bayes algorithm method, which has as classification types such as Gaussian, Bernoulli, and Multinomial. This is used to calculate the probability value of words in text documents that are included in hate speech or non-hate speech with its level of accuracy. This classification is made with the Python programming language. At the Classification data collection stage, there were 4393 data collected randomly on social media Twitter. The data was divided using a ratio of 80% - 20%, where 3514 text data were used as training data and 879 data were used as test data. This research resulted in an accuracy level of classification of hate speech texts of 80% resulting from the Multinomial Naive Bayes classification with a pre-processing process.

Item Type:	Thesis (Skripsi)
Uncontrolled Keywords:	Hate Speech, Naïve Bayes, Social Media, Twitter
Subjects:	H Social Sciences > HE Communications > HE1 Media Massa T Technology > TZ Technical Information
Divisions:	Fakultas Ilmu Komunikasi dan Informatika > Teknik Informatika
Depositing User:	NADIA ZERLINDA TAUFIK PUTRI
Date Deposited:	21 Feb 2023 03:17
Last Modified:	03 Mar 2023 01:05
URI:	http://eprints.ums.ac.id/id/eprint/110403

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