Klasifikasi Komentar Cyberbullying Pada Media Sosial Dengan Tf-Idf Dan Metode Support Vector Machine

Bahri, Hairul (2024) Klasifikasi Komentar Cyberbullying Pada Media Sosial Dengan Tf-Idf Dan Metode Support Vector Machine. Undergraduate thesis, Politeknik Negeri Jember.

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Abstract

This research aims to minimize the problem of cyberbullying on social media. Even though social media should be a place for expressing creativity and sharing information, there are still many users who abuse this platform to carry out bullying. Uncontrolled negative and provocative comments can cause physical and mental harm to the victim. Therefore, a system is needed to classify comments as a first step in detecting cyberbullying. This research will focus on TikTok social media involving the Support Vector Machine method, as well as the TF-IDF approach for feature extraction. The system will classify comments and categorize them into cyberbullying or noncyberbullying also can report and block the account detected bullying. The hope is that this research will become a reference for preventive actions and policies to increase safety and comfort using social media. Model learning using data from 650 comments, 325 cyberbullying categories and 325 noncyberbullying categories obtained accuracy, precision, recall and f1-score of 85%. The system can perform classification well and requires variations in the dataset to increase accuracy.

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