Sentiment analysis of government regulations regarding the implementation of reverse-transcriptase polymerase chain reaction (RT-PCR) during the Covid-19 pandemic in Indonesia (Case study: Air transportation mode)

Abstract

Many people express their opinions regarding policies or rules imposed by the government through Twitter social media. This study was conducted to determine public sentiment regarding the rules for implementing the reverse-transcriptase polymerase chain reaction (RT-PCR) test as one of the requirements for using air transportation. The research was conducted using the Random Forest and K-Nearest Neighbor (KNN) algorithms and the CRISP-DM research method. Twitter data used is classified into negative, neutral and positive. The results of the sentiment analysis showed that the KNN algorithm outperformed Random Forest with an accuracy value of 75.63% and the sampling technique used was stratified sampling. The results of the sentiment analysis show that the public has a negative sentiment towards the policies issued by the government.