Perbandingan Algoritma Regresi Linier dan Regresi Random Forest Dalam Memprediksi Kasus Positif Covid-19

Authors

  • Syakirah Fachid Universitas Nasional, Jakarta
  • Agung Triayudi Universitas Nasional, Jakarta

DOI:

https://doi.org/10.30865/mib.v6i1.3492

Keywords:

Covid 19, Machine Learning, Linear Regression, Random Forest Regression, RMSE

Abstract

The world is in an uproar by a virus that was first reported in Wuhan, China. This virus is reported to have a very wide spread in a short time, so that it spread throughout the world. The World Health Organization (WHO) has designated Corona Virus Disease 2019 as a global pandemic. In a short time the Covid 19 virus immediately spread to all corners of the world, including Indonesia. As a result of the spread of COVID-19 which soared, there was a stir in the community. This situation affects many sectors of the nation's life. Therefore, the researchers conducted this study to find out how much data were affected, died, and recovered from Covid 19 and how to analyze linear regression and forest random regression. The results of the author's research resulted in an RMSE value of 4310,952 MAPE 19.9 and an accuracy rate of 91.4% in the Linear Regression algorithm while the Random Forest algorithm produced an RMSE value of 4342,481, MAPE 16.2 and an accuracy rate of 91.3%. So it can be said that in this research linear regression is slightly better than random forest.

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Published

2022-01-25

Issue

Vol. 6 No. 1 (2022): Januari 2022

Section

Articles

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