Penerapan Data Mining Untuk Klasifikasi Pandemi Covid-19 Di Indonesia Dengan Algoritma Naïve Bayes

Abstract

Coronavirus is a group of viruses that can cause disease in animals or humans. Several types of corona viruses are known to cause respiratory infections in humans ranging from cold coughs to more serious ones such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). This increased accuracy can facilitate decision making and efforts to provide information and knowledge for the prevention of people who want to carry out activities in the province so that we can prevent earlier and not be exposed by covid-19 and we can predict that one month the case will increase affected by covid-19. In testing the covid-19 data with the Naïve Bayes algorithm, this study can be tested by predicting attributes that have 7 attributes and the test shows that the naïve Bayes algorithm can classify a covid-19 data in producing a 0.910 probability level which is where the results are quite good then 700 training tests were performed and 300 testing data produced the most optimal which had an Accuracy value of 100.00%, Precision 100.00% and Recall 100.00%.

Keywords: Corona Virus, Covid-19, Classification, Naive Bayes, Data Mining