Comparison of Algorithms for Cyberbullying Detection to Football Player in Social Media
DOI:
https://doi.org/10.31315/telematika.v21i3.11721Keywords:
Natural Language Processing (NLP), Cyberbullying Detection, Machine LearningAbstract
Purpose: to compareNaïve Bayes, Support Vector Machine(SVM), and K-Nearest Neighbor(KNN) algorithms for detecting cyberbullying that happen to football player in social media.
Design/methodology/approach: In the cyberbullying detection process, the steps involved are data collection, labeling, data preprocessing, feature extraction, modeling, and finally evaluation by comparing the accuracy values of the three methods used.
Findings/result: Based on the accuracy values obtained, Naive Bayes emerged as the algorithm with the highest accuracy at 78.6%, followed by Support Vector Machine (SVM) with an accuracy of 77.9%, and K-Nearest Neighbor (KNN) with an accuracy of 65.6%.
Originality/value/state of the art: This research discusses the comparison of algorithms for detecting cyberbullying in social media related to football players, an area that has not been addressed by other studies. Additionally, the preprocessing stage and the three algorithms used were also designed and chosen by the researchers themselves.
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