Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm

Abstract

The Indonesian government has enforced the New Normal rule in maintaining economic stabilization and also restraining the spread of the virus during the Covid 19 pandemic. This has become a hot topic of conversation on social media Twitter, many people think positive and negative.The research conducted is a representation of text mining and text processing using machine learning using the Naive Bayes Classifier classification method, the objective of the analysis is to determine whether public sentiment towards the New Normal policy is positive or negative, and also as a basis for measuring the performance of the TF-IDF feature extraction and N-gram in machine learning uses the Naive Bayes method.The results of this study resulted in the accuracy rate of the Naive Bayes method with the TF-IDF feature selection. The total accuracy was 81% with a Precision value of 78%, Recall 91%, and f1-Score 84%. The highest results were obtained from the use of the Naive Bayes and Trigram algorithm parameters, namely 84%, namely 84% Precision, 86% Recall, and 85% f1-Score. The Naive Bayes algorithm with the use of the trigram type N-Gram feature extraction shows a fairly good performance in the process of classifying public tweet data.

Implementation of K-Nearest Neighbor (K-NN) Algorithm For Public Sentiment Analysis of Online Learning

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“…With this service, people prefer to give their opinion through social media rather than convey it directly. Conversations between users on Twitter often contain random information, but can be exploited by 3rd parties [1] [2]. Twitter users until January 2013 had more than 500 million internet users registered on Twitter.…”

Building Social Capital Online During the COVID-19 Transition in Indonesia

“…The first COVID-19 case in Indonesia drew various responses on social media. This situation has made social media users give negative responses and sentiments to government authorities (Isnain, Marga, & Alita, 2021). The pessimistic view was shown through hashtags, quotes, captions, and other types of information (Chandrasekaran, Mehta, Valkunde, & Moustakas, 2020;Rahmanti, Ningrum, Lazuardi, Yang, & Li, 2021).…”

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