Journal of ICT Research and Applications ITB Bandung Vol. 14 No. 3 2020
Hate Speech Classification in Indonesian Language Tweets by Using Convolutional Neural Network

Journal of ICT Research and Applications ITB Bandung Vol. 14 No. 3 2020
Hate Speech Classification in Indonesian Language Tweets by Using Convolutional Neural Network

convolutional neural network (CNN); deep learning; hate speech;
Indonesian language; text classification.

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.

Dewa Ayu Nadia Taradhita & I Ketut Gede Darma Putra

DOI: 10.5614/itbj.ict.res.appl.2021.14.3.2

IRCS-ITB

23 Maret 2021

Sri Wahyuni

ISSN: 2337-5787

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Journal of ICT Research and Applications ITB Bandung Vol. 14 No. 3 2020