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Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 14 No. 2 (2020) · pp. 169–178

DOI
10.22146/ijccs.51743
ISSN
2460-7258, 1978-1520
Published
2020-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Currently, the discussion about hate speech in Indonesia is warm, primarily through social media. Hate speech is communication that disparages a person or group based on characteristics such as (race, ethnicity, gender, citizenship, religion and organization). Twitter is one of the social media that someone uses to express their feelings and opinions through tweets, including tweets that contain expressions of hatred because Twitter has a significant influence on the success or destruction of one's image.This study aims to detect hate speech or not hate Indonesian speech tweets by using the Bidirectional Long Short Term Memory method and the word2vec feature extraction method with Continuous bag-of-word (CBOW) architecture. For testing the BiLSTM purpose with the calculation of the value of accuracy, precision, recall, and F-measure.The use of word2vec and the Bidirectional Long Short Term Memory method with CBOW architecture, with epoch 10, learning rate 0.001 and the number of neurons 200 on the hidden layer, produce an accuracy rate of 94.66%, with each precision value of 99.08%, recall 93, 74% and F-measure 96.29%. In contrast, the Bidirectional Long Short Term Memory with three layers has an accuracy of 96.93%. The addition of one layer to BiLSTM increased by 2.27%.

Keywords

Collaboration network of the authors

23 authors · 34 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.

  • Universitas Gadjah Mada
  • Universitas Teknokrat Indonesia
  • Sebelas Maret University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Azhari SnUniversitas Gadjah Mada2
Yunita SariUniversitas Gadjah Mada2
Annisaa UtamiUniversitas Gadjah Mada2
Abram Setyo PrabowoUniversitas Gadjah Mada1
Yayang Eluis Bali MawartikaUniversitas Gadjah Mada1
Muhammad Asaduddin HazaziUniversitas Gadjah Mada1
Sigit PriyantaUniversitas Gadjah Mada1
Mhd. Theo Ari BangsaUniversitas Gadjah Mada1
Sri T. HartatiUniversitas Gadjah Mada1
Ainil MardiahUniversitas Gadjah Mada1
Nurman Satya MargaUniversitas Teknokrat Indonesia1
Debby AlitaUniversitas Teknokrat Indonesia1
Riszki Wijayatun PratiwiUniversitas Gadjah Mada1
Jepi SupriyantoUniversitas Teknokrat Indonesia1
Muhammad Pajar KharismaUniversitas Teknokrat Indonesia1
Sensa Gudya Sauma SyahraUniversitas Gadjah Mada1
Astrid Noviana ParadhitaSebelas Maret University1
Anny Kartika SariUniversitas Gadjah Mada1
Fadila Shely AmaliaUniversitas Gadjah Mada1
Dian EkapratiwiUniversitas Gadjah Mada1

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">Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection</dc:title>
	<dc:creator>Isnain, Auliya Rahman</dc:creator>
	<dc:creator>Sihabuddin, Agus</dc:creator>
	<dc:creator>Suyanto, Yohanes</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Hate Speech; LSTM; BiLSTM; Word2vec; CBOW; Skipgram; Twitter</dc:subject>
	<dc:description xml:lang="en-US">Currently, the discussion about hate speech in Indonesia is warm, primarily through social media. Hate speech is communication that disparages a person or group based on characteristics such as (race, ethnicity, gender, citizenship, religion and organization). Twitter is one of the social media that someone uses to express their feelings and opinions through tweets, including tweets that contain expressions of hatred because Twitter has a significant influence on the success or destruction of one's image.This study aims to detect hate speech or not hate Indonesian speech tweets by using the Bidirectional Long Short Term Memory method and the word2vec feature extraction method with Continuous bag-of-word (CBOW) architecture. For testing the BiLSTM purpose with the calculation of the value of accuracy, precision, recall, and F-measure.The use of word2vec and the Bidirectional Long Short Term Memory method with CBOW architecture, with epoch 10, learning rate 0.001 and the number of neurons 200 on the hidden layer, produce an accuracy rate of 94.66%, with each precision value of 99.08%, recall 93, 74% and F-measure 96.29%. In contrast, the Bidirectional Long Short Term Memory with three layers has an accuracy of 96.93%. The addition of one layer to BiLSTM increased by 2.27%.</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>2020-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/51743</dc:identifier>
	<dc:identifier>10.22146/ijccs.51743</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 14, No 2 (2020): April; 169-178</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/51743/27886</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 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>
</oai_dc:dc>