<![CDATA[Twitter is one of the popular social media used to get news quickly and briefly. After the outbreak of the COVID-19 virus and the government's policy to vaccinate against COVID-19 in Indonesia, more and more public opinion has been expressed through tweets. This makes the topic of COVID-19 vaccination interesting for sentiment analysis. Through sentiment analysis, information in the form of text data can be extracted to classify information related to positive or negative opinions. In this study, the classification of public opinion on COVID-19 vaccination was carried out using the supporting vector machine method without and with lexicon-based features. The manual labeling data used were 2981 tweets. The results of the classification of public opinion on COVID-19 vaccination in Indonesia with a supporting vector machine without the lexicon feature obtained accuracy, g-mean and AUC of 83%, 50% and 61.35%, respectively. Meanwhile, with lexicon-based features, the performance of the supporting vector machine method for classifying public opinion on COVID-19 vaccination in Indonesia obtained accuracy, g-mean and AUC of 90%, 86.63% and 87%, respectively. Based on these results, the performance of the supporting vector machine method with lexicon-based features provides better results for the performance of classifying of public opinion on COVID-19 vaccination compared to supporting vector machines without lexicon-based features.]]>
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