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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Daniel Septian Feri Bancin
Universitas Prima Indonesia
Computer Science & IT Engineering

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PEMANFAATAN MACHINE LEARNING MODEL INDOBERT UNTUK MENGIDENTIFIKASI CYBERBULLYING PADA KOLOM KOMENTAR SOSIAL MEDIA YOUTUBE: artikel Hendra Pasaribu; Daniel Septian Feri Bancin; Dian Karina Sembiring; Joyakim Simarmata; Vrendy Gusman Gulo
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8092

Abstract

The growth of social media, particularly YouTube, has led to increased user interaction through comment sections. The high volume of comments has also given rise to various forms of negative content, such as insults, harassment, sarcasm, and verbal attacks, which can be categorized as Cyberbullying. Therefore, an automated system capable of identifying Cyberbullying comments is needed. This study aims to develop a Cyberbullying detection system for Indonesian-language YouTube comments using the IndoBERT model. The dataset was collected through a YouTube comment scraping process using the YouTube Data API from Indonesian content videos published between 2019 and 2024. The research stages included data preprocessing, manual labeling, dataset balancing, tokenization, model training, model evaluation, and the implementation of a web-based system using Laravel and FastAPI. The final dataset consisted of 9,298 comments categorized into Cyberbullying, Non-Bullying, and Undifine/Invalid Data classes. The training process utilized the indobenchmark/indobert-base-p1 model with an 80:20 split between training and testing data. Based on the evaluation results, the model achieved an accuracy of 81.45%, precision of 85.49%, recall of 79.64%, F1-score of 81.87%, and an AUC score of 93.90%. These results indicate that the IndoBERT model is capable of effectively classifying Cyberbullying comments in Indonesian-language YouTube comments. Furthermore, the developed system can assist in the automatic identification of Cyberbullying comments and demonstrates that the IndoBERT model is effective for analyzing Indonesian-language social media text.
Co-Authors Dian Karina Sembiring Hendra Pasaribu Joyakim Simarmata Vrendy Gusman Gulo