Classification of Covid-19 vaccine data screening with Naive Bayes algorithm using Knowledge Discovery in database method

Abstract

Acute Respiratory Syndrome Coronavirus-2 (SARS-Cov-2) known as covid-19 was detected and caused a very large number of deaths due to a mysterious respiratory disease. With the death toll continuing to rise, the government was forced to take swift action to break the chain of spread and reduce the number of deaths by taking vaccinations. An adequate vaccine against Covid-19 is expected to vaccinate at least 70% of the population. Therefore, this study was carried out as a step to help break the chain of the spread of the Covid-19 virus, by classifying the Covid-19 vaccine screening data. The research method applied in this study is the Knowledge Discovery in Database (KDD) method, in which there are several processes, namely selection, pre-processing, transformation, data mining, and evaluation. The application of the Naive Bayes method is expected to be able to classify Covid-19 vaccine screening data with vaccine class values, no, and delay. The results of the research on the classification of the Naive Bayes method show that there are 959 data with Vaccine data 695, No 200, and Delay 64. Processed using the Rapidminer application, the accuracy is 96.56%, Precision is 92.46%, and Recall is 92.13%.

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