The Naive Bayes Algorithm in Predicting the Spread of the Omicron Variant of Covid-19 in Indonesia: Implementation and Analysis

Jeffri Prayitno Bangkit Saputra, Racidon P Bernarte

Abstract


Indonesia was struck by an epidemic of the corona virus in the start of March 2020, according to official reports (covid). Indonesia continues to see a rise in the number of cases of covid-19 spreading on a daily basis. The general people are urged to engage in social distancing in order to disrupt the development of COVID-19, which has spread across Indonesia's numerous areas. For this reason, this research was undertaken as a preemptive step against the Covid-19 pandemic by estimating the extent of the Omicron variety of Covid-19's spread around the world, with a particular emphasis on Indonesia. The research methodologies employed in this study were problem analysis and literature review, as well as data gathering and execution. The Naive Bayes technique is thought to be capable of estimating the degree of COVID-19 dissemination in Indonesia. The results of the Naive Bayes method classification study revealed that 16 data from 33 data tested for Covid-19 cases per province were correctly classified with an accuracy of 46.4252 percent, while 16 data from 33 data tested for Covid-19 cases per province were misclassified with an accuracy of 46.4252 percent.


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Keywords


Covid-19; Naive Bayes; Machine Learning; Data Mining

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