Studi Performa TF-IDF dan Word2Vec Pada Analisis Sentimen Cyberbullying

Ahmad Hilman Dani; Eva Yulia Puspaningrum; Retno Mumpuni

doi:10.62951/router.v2i2.76

Authors

Ahmad Hilman Dani Universitas Pembangunan Nasional “Veteran” Jawa Timur
Eva Yulia Puspaningrum Universitas Pembangunan Nasional “Veteran” Jawa Timur
Retno Mumpuni Universitas Pembangunan Nasional “Veteran” Jawa Timur

DOI:

https://doi.org/10.62951/router.v2i2.76

Keywords:

Cyberbullying, Sentiment Analysis, SVM, TF-IDF, Word2Vec

Abstract

On August 14, 2023, Indonesia had approximately 228 million social media users, a number that is expected to continue growing to reach 267 million by 2028. Social media can be used to spread both positive and negative information, and one of the various negative effects is cyberbullying. Consequently, much research is conducted in the field of machine learning to develop sentiment analysis. One crucial step in sentiment analysis is word weighting. The two most common word weighting methods are TF-IDF and Word2Vec. These methods can be compared to determine which one produces better classification results, allowing cyberbullying sentiments on social media to be detected more accurately. Based on nine test scenarios, the final results showed that TF-IDF performed better than Word2Vec in this study, with an accuracy of 84%.

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