NAÏVE BAYES ALGORITHM CLASSIFICATION IN SENTIMENT ANALYSIS COVID-19 WIKIPEDIA

Jessica Margaret Br Sembiring; Hendry h

doi:10.20884/1.jutif.2022.3.4.311

Jessica Margaret Br Sembiring Program Studi Teknik Informatika, Universitas Kristen Satya Wacana, Indonesia
Hendry Program Studi Teknik Informatika, Universitas Kristen Satya Wacana, Indonesia

DOI: https://doi.org/10.20884/1.jutif.2022.3.4.311

Keywords: Classification, COVID-19, Naïve Bayes Classifier, Sentimen Analysis, Text Mining, Wikipedia

Abstract

In recent years during the pandemic Wikipedia created more than 5,200 new pages regarding COVID-19 cases, with an accumulation of more than 400 million pages by mid-June 2020. Wikipedia is one of the most popular websites of our time. In this case Wikipedia always integrates new and fast research. To get an opinion from wikipedia text, sentiment analysis is needed. The analysis was conducted using a classification containing public sentiment regarding the issue of COVID-19 in Indonesia. The classification method used in this study is naive bayes classifier (NBC). Naïve Bayes Classifier is a popular method of solving classification problems. This classification method is often used in sentiment analysis in both precision and data computing. This wikipedia classification is obtained from each label, namely positive, negative and neutral classes. The results of tests conducted in the classification of naive bayes get a high accuracy of 81%.

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