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All Journal International Journal of New Media Technology
Muhamad Riza Kurniawanda
Universitas Multimedia Nusantara
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Analysis Sentiment Cyberbullying In Instagram Comments with XGBoost Method Muhamad Riza Kurniawanda; Fenina Adline Twince Tobing
IJNMT (International Journal of New Media Technology) Vol 9 No 1 (2022): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v9i1.2670

Abstract

Technological developments make social media widely used by the general public, which causes negative impacts, one of which is cyberbullying. Cyberbullying is an act of insulting, humiliating another person on social media. A system that can detect cyberbullying because of the large amount of information circulating on social media is impossible for humans to visit. One suitable method to solve this problem is Extereme Gradient Boosting (XGBoost). XGBoost was chosen because it can run 10 times faster than other Gradient Boosting methods. The process of changing sentences into vectors uses the TF-IDF method. The TF/IDF method is known as a simple but relevant algorithm in doing words on a document. XGBoost accepts input in the form of vectors obtained from the TF-IDF process. In this research, there are 1452 comments which will be broken down into training data and testing data. By using XGBoost and TF-IDF methods, the accuracy is 75.20%, precision is 71%, recall is 87%, and F1-score is 78%.
Co-Authors Fenina Twince Tobing