Sentiment Analyst on Twitter Using the K-Nearest Neighbors (KNN) Algorithm Against Covid-19 Vaccination

Authors

  • Suprayogi Suprayogi Dian Nuswantoro University
  • Christy Atika Sari Dian Nuswantoro University
  • Eko Hari Rachmawanto Dian Nuswantoro University

DOI:

https://doi.org/10.33633/jais.v7i2.6734

Abstract

The corona virus (2019-nCoV), commonly known as COVID-19 has been officially designated as a global pandemic by the WHO. Twitter, is one of the social media used by many people and is popular among internet users in expressing opinions. One of the problems related to Covid-19 and causing a stir is the procurement of the Covid-19 vaccine. The procurement of the vaccine caused various opinions in Indonesian society, where the uproar was also quite busy being discussed on Twitter and even became a Trending Topic. The opinions that appear on Twitter will then be used as data for the Sentiment Analysis process. One of the members of the House of Representatives (DPR), namely RibkaTjiptaning was also included in the Trending Topic list on Twitter for refusing to receive the Covid-19 vaccine. Sentiment analysis itself is a computational study of opinions, sentiments and emotions expressed textually. Sentiment analysis is also a technique to extract information in the form of a person's attitude towards an issue or event by classifying the polarity of a text. Research related to Sentiment Analysis will be examined by dividing public opinion on Twitter social media into positive and negative sentiments, and using the K-Nearest Neighbor (KNN) algorithm to classify public opinion about COVID-19 vaccination. In the testing section, the Confusion Matrix method is used which then results in an accuracy of 85%, precision of 100%, and recall of 78.94%.

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Published

2022-09-07

Issue

Vol. 7 No. 2 (2022): Journal of Applied Intelligent System

Section

Articles

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