Analisis Sentimen Opini Publik pada Media Sosial Twitter terhadap Vaksin Covid-19 menggunakan Algoritma Support Vector Machine dan Term Frequency-Inverse Document Frequency - Edgar Maulana Thoriq; Dian Eka Ratnawati; Bayu Rahayudi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2021)
Social media is a place for people to express their aspirations, ideas, and even their critics. One of the policies made recently by the government is the provision of COVID-19 vaccine. This policy has been widely discussed on Twitter and attracted a lot of diverse opinions in the society. Twitter is a social media that has a fairly large user base in Indonesia, where many users share their opinions regarding the provision of COVID-19 vaccine. Twitter can be a source of data that can be used to conduct sentiment analysis on government policies by classifying tweets (a term for content in Twitter) into positive or negative categories. The classification process is utilizing a classification algorithm, namely Support Vector Machine and term weighting namely Term Frequency - Inverse Document Frequency (TF-IDF) method. This study uses 450 tweets, then testing is carried out using the cross validation method with number of fold = 10. Best performance of the classification algorithm is 86% accuracy, 88% precision, 82% recall, and 85% f-measure. Value of the performance is obtained with value C of 1 and the maximum iteration of 300.