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All Journal Malcom: Indonesian Journal of Machine Learning and Computer Science
Fatin, Alfindian Kurnia
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Computer Science & IT

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Sentiment Analysis of Cyberbullying Against Children on Social Media: A Naïve Bayes Approach Fatin, Alfindian Kurnia; Adhitama, Rifki; Istighosah, Maie
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2858

Abstract

Cyberbullying involving children has become a major concern on social media, generating diverse public responses. This study aims to analyze public sentiment toward child cyberbullying cases on social media and to evaluate the effectiveness of data-balancing techniques in improving the performance of a Naïve Bayes (NB) classifier. The motivation for using NB lies in its computational efficiency, strong performance in text classification, and suitability for high-dimensional Term Frequency–Inverse Document Frequency (TF-IDF) features. A dataset consisting of 3,137 comments was collected from X/Twitter and processed through text preparation, lexicon-based sentiment labeling using Indonesian Sentiment Lexicon (InSet), and TF-IDF feature representation. Three balancing scenarios, namely Random Oversampling, Random Undersampling, and Non-Balancing, were compared by varying the proportion of training and testing samples across four split ratios: 90:10, 80:20, 70:30, and 60:40. The experimental results show that balancing techniques contribute to classification effectiveness, with Random Oversampling producing the best outcome. The highest accuracy of 86.36% was achieved with Random Oversampling and a 90:10 training-test split, outperforming Random Undersampling and Non-Balancing. Furthermore, the sentiment distribution indicates that negative sentiment dominates public responses to child cyberbullying incidents. 
Co-Authors Maie Istighosah Rifki Adhitama, Rifki