Multilayer Perceptron and TF-IDF in the Classification of Hate Speech on Twitter in Indonesian


  • Akmal Syahrandi Universitas Muhammadiyah Kalimantan Timur
  • Asslia Johar Latipah Universitas Muhammadiyah Kalimantan Timur
  • Naufal Azmi Verdikha Universitas Muhammadiyah Kalimantan Timur



hate speech, Multilayer Perceptron, TF-IDF


Twitter nowadays is one of the popular social media which currently has over 300millions accounts, twitter is the rich source to learn about people’s opion and sentimental analysis. However, this also brings new problems where the practice of hate speech. This research classifies of hate speech on social media. Evaluation using dataset from previous research Ibrohim&Budi (2019), then using classification method Multilayer Perceptron which combined with feature extraction to be able to detect negations and weighting uses Term Frequency – Inverse Document Frequency (TF-IDF). Results show that the F1 score gives an accuracy rate of up to 74.51%. This research has a reasonably good effectiveness from combining the TF-IDF and Multilayer Perceptron methods, considering the results obtained from the F1 Score evaluation value.


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How to Cite

Syahrandi, Akmal, Asslia Johar Latipah, and Naufal Azmi Verdikha. 2023. “Multilayer Perceptron and TF-IDF in the Classification of Hate Speech on Twitter in Indonesian”. JSE Journal of Science and Engineering 2 (1):17-22.



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