MODEL OF CYBERBULLYING DETECTION ON SOCIAL MEDIA USING MULTI-LABEL DEEP LEARNING: A COMPARATIVE STUDY

Authors

  • Lemi Iryani Universitas sjakhyakirti
  • Junaidi Junaidi
  • Paisal Paisal
  • Mariana Purba
  • Nia Umilizah
  • Bakhtiar Bakhtiar
  • Nur Ani

DOI:

https://doi.org/10.33480/jitk.v10i4.6004

Keywords:

BiLSTM, CNN-1D, cyberbullying, LSTM, RNN

Abstract

Cyberbullying is the deliberate act of using technology to harm others. This study aims to analyze 400 Instagram comments obtained via API from previous research. The data were labeled into three classes: negative (containing cyberbullying), positive (non-bullying, supportive), and neutral (neither positive nor negative). The data for experiment was divided into 70% for training and 30% for testing. The research methodology consists of three main stages. The first stage is text preprocessing, which includes tokenization (splitting comments into tokens), filtering (removing unimportant words or stop-words), and stemming (converting words with affixes into their root forms). The second stage is classification analysis using BiLSTM, LSTM, RNN, and CNN-1D methods. The third stage is evaluation by comparing the model's classification results with manually labeled data using accuracy as the evaluation metric. The results show that the BiLSTM model performed the best, achieving an accuracy of 98.51% on the training data and 81.82% on the testing data. The BiLSTM method used in this study can be further adapted to enhance the effectiveness of cyberbullying detection in various applications.

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Published

2025-05-30

How to Cite

[1]
L. Iryani, “MODEL OF CYBERBULLYING DETECTION ON SOCIAL MEDIA USING MULTI-LABEL DEEP LEARNING: A COMPARATIVE STUDY”, jitk, vol. 10, no. 4, pp. 742–748, May 2025.

Issue

Vol. 10 No. 4 (2025): JITK Issue May 2025

Section

Articles

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Copyright (c) 2025 Lemi Iryani, Junaidi Junaidi, Paisal Paisal, Mariana Purba, Nia Umilizah, Bakhtiar Bakhtiar, Nur Ani

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.