Implementasi Algoritma Naive Bayes Untuk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia

  • Alvina Felicia Watratan STMIK Profesional Makassar
  • Arwini Puspita. B STMIK Profesional Makasar
  • Dikwan Moeis STMIK Profesional Makasar
Keywords: Covid-19, Naïve Bayes, Aplikasi WEKA

Abstract

The COVID-19 pandemic is the first and foremost health crisis in the world.  Coronavirus is a collection of viruses from the subfamily Orthocronavirinae in the Coronaviridae family and the order of Nidovirales.  This group of viruses that can cause disease in birds and mammals, including humans.  In humans, coronaviruses cause generally mild respiratory infections, such as colds, although some forms of disease such as;  SARS, MERS, and COVID-19 are more deadly. Anticipating and reducing the number of corona virus sufferers in Indonesia has been carried out in all regions.  Among them by providing policies to limit activities out of the house, school activities laid off, work from home (work from home), even worship activities were laid off.  This has become a government policy based on considerations that have been analyzed to the maximum, of course. Therefore this research was carried out as an anticipation step towards the Covid-19 pandemic by predicting the spread of Covid-19, especially in Indonesia. The research method applied in this research is problem analysis and literature study, collecting data and implementation.  The application of the naive bayes method is expected to be able to predict the spread rate of COVID-19 in Indonesia. The results of the Naive Bayes method classification show that 16 data from 33 data were tested in Covid-19 cases per province with an accuracy of 48.4848%, where of the 33 data tested in the Covid-19 case per province tested there were 16 data that were successfully classified correctly.

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Published
2020-07-12
How to Cite
Alvina Felicia Watratan, Arwini Puspita. B, & Dikwan Moeis. (2020). Implementasi Algoritma Naive Bayes Untuk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia. Journal of Applied Computer Science and Technology, 1(1), 7 - 14. Retrieved from https://journal.isas.or.id/index.php/JACOST/article/view/9
Section
Articles