DOI: https://doi.org/10.14710/jtsiskom.2021.13907

Algoritme decision tree untuk mendeteksi ujaran kebencian dan bahasa kasar multilabel pada Twitter berbahasa Indonesia

Decision tree algorithm for multi-label hate speech and abusive language detection in Indonesian Twitter

Department of Informatics, UIN Sultan Syarif Kasim Riau. Jl. H.R. Soebrantas km 11.5 Simpang Baru Panam, Pekanbaru, Riau 28293, Indonesia

Received: 7 Sep 2020; Revised: 4 Jun 2021; Accepted: 8 Aug 2021; Published: 31 Oct 2021.
Open Access Copyright (c) 2021 The authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to cite (IEEE): F. Ihsan, I. Iskandar, N. S. Harahap, and S. Agustian, "Algoritme decision tree untuk mendeteksi ujaran kebencian dan bahasa kasar multilabel pada Twitter berbahasa Indonesia," Jurnal Teknologi dan Sistem Komputer, vol. 9, no. 4, pp. 199-204, Oct. 2021. https://doi.org/10.14710/jtsiskom.2021.13907
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Abstract
Hate speech and abusive language are easily found in written communications in social media like Twitter. They often cause a dispute between parties, the victims, and the first who write the tweet. However, it is also difficult to distinguish whether a tweet contains hate speech and/or abusive language for those who take sides. This research aims to develop a method to classify the tweets into abusive and/or contain hate speech classes. If hate speech is detected, then the system will measure the hardness level of hatred. The dataset includes 13,126 real tweets data. Word embeddings are used for featuring text input. For the tweets classification, we use a Decision Tree algorithm. Some engineering of features and parameters tuning has improved the classification of the three classes: hate speech class, abusive words, and hate speech level. The lexicon feature in the Decision Tree classification produces the highest accuracy for detecting the three classes rather than engineering special features and textual features. The average accuracy of the three classes increased from 69.77 % to 70.48 % for the training-testing composition of 90:10, and another 69.35 % to 69.54 % for 80:20 respectively.
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Keywords: hate speech; abusive language; decision tree; Twitter; word embeddings
Funding: UIN Sultan Syarif Kasim Riau

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Language : ID
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