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Hate Speech Classification in Indonesian Language Tweets by Using Convolutional Neural Network

Journal of ICT Research and Applications (JICTRA) · Vol. 14 No. 3 (2021) · pp. 225–239

DOI
10.5614/itbj.ict.res.appl.2021.14.3.2
ISSN
2338-5499, 2337-5787
Published
2021-02-23
Publisher
DRI - ITB
Language
eng

Abstract

The rapid development of social media, added with the freedom of social media users to express their opinions, has influenced the spread of hate speech aimed at certain groups. Online based hate speech can be identified by the used of derogatory words in social media posts. Various studies on hate speech classification have been done, however, very few researches have been conducted on hate speech classification in the Indonesian language. This paper proposes a convolutional neural network method for classifying hate speech in tweets in the Indonesian language. Datasets for both the training and testing stages were collected from Twitter. The collected tweets were categorized into hate speech and non-hate speech. We used TF-IDF as the term weighting method for feature extraction. The most optimal training accuracy and validation accuracy gained were 90.85% and 88.34% at 45 epochs. For the testing stage, experiments were conducted with different amounts of testing data. The highest testing accuracy was 82.5%, achieved by the dataset with 50 tweets in each category.

Keywords

Collaboration network of the authors

11 authors · 22 co-authorship links · 4 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Udayana University
  • University of Indonesia
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ida Ayu Dwi GiriantariUdayana University2
Made SudarmaUdayana University1
P M PrihatiniUdayana University1
Dewa Made Sri ArsaUniversity of Indonesia1
I Gusti Ngurah Dwiva HardijayaUdayana University1
I Gede Galang Surya PrabawaUdayana University1
I Made Aris Satia WidiatmikaUdayana University1
Risky Aswi RamadhaniUdayana University1
Made SudarmaUdayana University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Hate Speech Classification in Indonesian Language Tweets by Using Convolutional Neural Network</dc:title>
	<dc:creator>Taradhita, Dewa Ayu Nadia</dc:creator>
	<dc:creator>Putra, I Ketut Gede Darma</dc:creator>
	<dc:subject xml:lang="en-US">convolutional neural network</dc:subject>
	<dc:subject xml:lang="en-US">deep learning</dc:subject>
	<dc:subject xml:lang="en-US">hate speech</dc:subject>
	<dc:subject xml:lang="en-US">Indonesian language</dc:subject>
	<dc:subject xml:lang="en-US">text classification</dc:subject>
	<dc:description xml:lang="en-US">The rapid development of social media, added with the freedom of social media users to express their opinions, has influenced the spread of hate speech aimed at certain groups. Online based hate speech can be identified by the used of derogatory words in social media posts. Various studies on hate speech classification have been done, however, very few researches have been conducted on hate speech classification in the Indonesian language. This paper proposes a convolutional neural network method for classifying hate speech in tweets in the Indonesian language. Datasets for both the training and testing stages were collected from Twitter. The collected tweets were categorized into hate speech and non-hate speech. We used TF-IDF as the term weighting method for feature extraction. The most optimal training accuracy and validation accuracy gained were 90.85% and 88.34% at 45 epochs. For the testing stage, experiments were conducted with different amounts of testing data. The highest testing accuracy was 82.5%, achieved by the dataset with 50 tweets in each category.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2021-02-23</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
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	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/15056</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2021.14.3.2</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 14 No. 3 (2021); 225-239</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/15056/5133</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Journal of ICT Research and Applications</dc:rights>
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