Analisis Sentimen Masyarakat Indonesia Terkait Vaksin Covid-19 Pada Media Sosial Twitter Menggunakan Metode Support Vector Machine (Svm)

Muhammad Fadilah Arfat; Styawati Styawati; Andi Nurkholis; Indra Kurniawan

doi:10.30591/jpit.v7i2.3549

Analisis Sentimen Masyarakat Indonesia Terkait Vaksin Covid-19 Pada Media Sosial Twitter Menggunakan Metode Support Vector Machine (Svm)

Muhammad Fadilah Arfat, Styawati Styawati, Andi Nurkholis, Indra Kurniawan

Abstract

COVID-19 is a new disease outbreak that has been officially designated as a global pandemic by the Worldi Health Organizationi (WHO) oni March 11, 2020. Seeing the rapid development of COVID-19, the Government of Indonesia has carried out vaccinations that have been carried out since January 13, 2021, this vaccination is prioritized for medical personnel and red zone areas. Since its emergence, therei have been many prosi andi consi regardingi the vaccination process and it has alsoi become a trending topici on sociali media Twitter oni January 13, 2021. Onei of the mosti widely used social media by Indonesiani people isi twitter sociali media. According to We arei Social sources in 2020, twitteri social media is rankedi fifth in the category of sociali media that is often used with a user percentage of 56% after Youtube, Whatsapp, Facebook as well as Instagram. Thisi shows that there is a huge opportunity for data sources that can be usedi to find out the positive and negativei sentiments of the related community, which is useful for interested parties to carry out evaluations. So that it can see how many people agree and disagree. If the percentage of people who disagree is more, the government must do better socialization so that people can better understand and not feel afraid of the vaccine. This study aims to find out how public sentiment is about the government's policies regarding the COVID-19 vaccinei using the Support Vector Machine method. by extracting the tf-idf feature and comparing the kernels contained in the SVM, including Linear, RBF, Polynomial, and Sigmoid. With tests that will later see how the values of accuracy, precision, recall and F1-Score are.

Keywords

Analisis Sentimen, SVM, Twitter, Vaksin, Covid-19

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DOI: https://doi.org/10.30591/jpit.v7i2.3549