ANALISIS SENTIMEN TWITTER TERHADAP KENAIKAN HARGA BAHAN BAKAR MINYAK INDONESIA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

Abstract

Sentiment analysis is a process that aims to determine positive or negative polarity. Twitter, as a social media platform, is used as a space for public information and opinions in responding to issues, including the increase in fuel prices. The government officially increased fuel prices, which was met with criticism from the public. This study aims to categorize the sentiment polarities of the public and determine the accuracy level in sentiment analysis. The method used in this study is Support Vector Machine with linear, RBF, polynomial, and sigmoid kernels, with the linear kernel being obtained as the best kernel. Based on the results of the testing done on sentiment data related to the increase in fuel prices in Indonesia on Twitter, which consists of 1130 data (485 positive and 645 negative), evaluation was done using a confusion matrix to see how well the model can classify correctly. The results show that the Support Vector Machine method produces an accuracy rate of 88.49%.