Analisa Efektifitas Kebijakan PPKM terhadap Pertumbuhan Kasus COVID-19 Menggunakan Algoritma Naïve Bayes

 (*)Regiolina Hayami Mail (Universitas Muhammadiyah Riau, Pekanbaru, Indonesia)
 Yulia Fatma (Universitas Muhammadiyah Riau, Pekanbaru, Indonesia)
 Okta Tri Antoni (Universitas Muhammadiyah Riau, Pekanbaru, Indonesia)
 Harun Mukhtar (Universitas Muhammadiyah Riau, Pekanbaru, Indonesia)

(*) Corresponding Author

Submitted: June 23, 2022; Published: July 25, 2022

Abstract

The pandemic that is being experienced by Indonesia, namely the outbreak of the COVID-19 virus, has led to the implementation of large-scale social restrictions in order to accelerate the handling of the spread of the virus. As a result of the increasing number of COVID-19 cases in Indonesia, including in Riau Province, where every City and Regency has increased, especially Pekanbaru, the implementation of Community Activity Restrictions (PPKM). This study tries to answer this question by using the Naïve Bayes Algorithm. Naïve Bayes Classifier is a probabilistic and statistical method of classification technique that predicts future opportunities based on previous experience. The use of the Naïve Bayes algorithm to predict the growth of Covid-19 cases in Pekanbaru obtained a good performance score with 90.00% accuracy, 90.24% precision and 91.90% recall. Based on the results of the classification of the datasets used in this study, it can be concluded that the implementation of the Policy for Enforcement of Community Activity Restrictions (PPKM) in the city of Pekanbaru has proven to be effective where there has been a decline in the category of growth of Covid-19 cases with a high category from 70% to 25%. Vice versa, the growth of Covid-19 cases in the low category has increased from 30% to 65%.

Keywords


Implementation of the Policy for Enforcement of Community Activity Restrictions (PPKM); Machine Learning; Naïve Bayes; Rapidminer

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