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Hate Speech Detection for Indonesia Tweets Using Word Embedding And Gated Recurrent Unit

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 13 No. 1 (2019) · pp. 43–52

DOI
10.22146/ijccs.40125
ISSN
2460-7258, 1978-1520
Published
2019-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Social media has changed the people mindset to express thoughts and moods. As the activity of social media users increases, it does not rule out the possibility of crimes of spreading hate speech can spread quickly and widely. So that it is not possible to detect hate speech manually. GRU is one of the deep learning methods that has the ability to learn information relations from the previous time to the present time. In this research feature extraction used is word2vec, because it has the ability to learn semantics between words. In this research the GRU performance will be compared with other supervision methods such as support vector machine, naive bayes, decision tree and logistic regression. The results obtained show that the best accuracy is 92.96% by the GRU model with word2vec feature extraction. The use of word2vec in the comparison supervision method is not good enough from tf and tf-idf.

Keywords

Collaboration network of the authors

38 authors · 52 co-authorship links · 15 across different organisations

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

  • Universitas Gadjah Mada
  • Indonesia University of Education
  • Universitas Stikubank
  • University of Indonesia
  • Universitas Airlangga
  • Instituto Profissional de Canossa - IPDC
  • Universitas Halu Oleo
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Retantyo WardoyoUniversitas Gadjah Mada4
Khabib MustofaUniversitas Gadjah Mada4
Edhy SutantaUniversitas Gadjah Mada4
Agus HarjokoUniversitas Gadjah Mada3
Sandi Fajar RodiyansyahIndonesia University of Education2
Edy WinarnoUniversitas Stikubank2
Aniati Murni ArymurthyUniversity of Indonesia2
Mardi Siswo UtomoUniversitas Gadjah Mada1
Badrus ZamanUniversitas Airlangga1
Agustinus JacobusUniversitas Gadjah Mada1
Sulistyowati SulistyowatiAffiliation unknown1
Noviah Dwi PutrantiUniversitas Gadjah Mada1
Ambarwati AmbarwatiInstituto Profissional de Canossa - IPDC1
Agus Sasmito AriwibowoAffiliation unknown1
Eka KaryawatiUniversitas Gadjah Mada1
Robynson AmsekeAffiliation unknown1
Natalis RansiUniversitas Halu Oleo1
Sumarni AdiUniversitas Gadjah Mada1
Lutfi Budi IlmawanState University of Malang1
Wayan Gede Suka ParwitaUniversitas Gadjah Mada1
Fadhila Tangguh AdmojoUniversitas Gadjah Mada1
Siti KhomsahInstitut Teknologi Telkom Purwokerto1
Doni SetyawanUniversitas Gadjah Mada1
Fajar RatnawatiPoliteknik Negeri Bengkalis1
Ira ZulfaUniversitas Gadjah Mada1
Rakhmad IkhsanudinUniversitas Gadjah Mada1
Wafda RifaiUniversitas Gadjah Mada1
Sigit PriyantaUniversitas Gadjah Mada1
Sholikin Ady ChandraUniversitas Gadjah Mada1
Putri Rizki AmaliaUniversitas Gadjah Mada1
Angel Berta Desi SuryantiUniversitas Gadjah Mada1
Fahmi Reza PrasastioUniversitas Gadjah Mada1
Azhari AzhariUniversitas Gadjah Mada1
Anak Agung Istri Ngurah Eka KaryawatiUdayana University1
Arif RahmanUniversitas Gadjah Mada1
Khabis MustofaAffiliation unknown1

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://journal.ugm.ac.id/ijccs/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 Detection  for Indonesia  Tweets Using Word  Embedding And Gated Recurrent Unit</dc:title>
	<dc:creator>Patihullah, Junanda</dc:creator>
	<dc:creator>Winarko, Edi</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Gated Recurrent Unit;Hate Speech Detection;Word2vec;RNN;Word Embedding</dc:subject>
	<dc:description xml:lang="en-US">Social media has changed the people mindset to express thoughts and moods. As the activity of social media users increases, it does not rule out the possibility of crimes of spreading hate speech can spread quickly and widely. So that it is not possible to detect hate speech manually. GRU is one of the deep learning methods that has the ability to learn information relations from the previous time to the present time. In this research feature extraction used is word2vec, because it has the ability to learn semantics between words. In this research the GRU performance will be compared with other supervision methods such as support vector machine, naive bayes, decision tree and logistic regression. The results obtained show that the best accuracy is 92.96% by the GRU model with word2vec feature extraction. The use of word2vec in the comparison supervision method is not good enough from tf and tf-idf.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2019-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/40125</dc:identifier>
	<dc:identifier>10.22146/ijccs.40125</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 13, No 1 (2019): January; 43-52</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/40125/23718</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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