Analisa Sentimen Perkembangan Vtuber Dengan Metode Support Vector Machine Berbasis Smote

Normah Normah, Bakhtiar Rifai, Satrio Vambudi, Rifki Maulana


Vtuber (Virtual Youtuber) is a content creator who creates content for the YouTube platform. Unlike other content creators, Vtuber uses 2D or 3D animated characters to interact with viewers. Vtubers usually use iconic anime characters to represent them, this is intended to attract viewers who usually come from Weaboo circles or commonly called Otaku. For this reason, it is necessary to make a sentiment analysis about vtuber to provide knowledge about vtuber for the Indonesian. The purpose of this study is to be able to model sentiment classification using the Support Vector Machine (SVM) method using a balancing method, namely the Synthetic Minority Oversampling Technique (SMOTE) found in RapidMiner, and to find out the vtuber trend in Indonesia based on Twitter can be done to find out how many people think about vtuber in this regard can be done by utilizing the API provided by twitter. From the results of the classification using a dataset of 321 comments data, it is known that there are 220 positive data and 101 negative data, resulting in an accuracy of 88.18% and 89% positive


sentiment analyst, smote, svm

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