COMPARISON OF LINEAR REGRESSIONS AND NEURAL NETWORKS FOR FORECASTING COVID-19 RECOVERED CASES

  • Tyas Setiyorini (1) Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri
  • Frieyadie Frieyadie (2*) Universitas Nusa Mandiri

  • (*) Corresponding Author
Keywords: Covid-19, Forecasting, Linear Regression, Neural Network

Abstract

The emergence of the Covid-19 outbreak for the first time in China killed thousands to millions of people. Since the beginning of the emergence of the number of cases of Covid-19 continues to increase until now. The increase in Covid-19 cases has a very bad impact on health, social and economic life. The need for future forecasting to predict the number of deaths and recoveries from cases that occur, so that the government and the public can understand the spread, prevent and plan actions as early as possible. Several previous studies have forecast the future impact of Covid-19 using the Machine Learning method. Time series forecasting can be done using traditional methods with Linear Regression or Artificial Intelligent methods with neural networks. In this study, it has been proven that there is a linear relationship in the time series data of Covid-19 recovered cases in China, so it is proven that the performance of Linear Regression is better than the Neural Network.

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Published
2022-06-20
How to Cite
Setiyorini, T., & Frieyadie, F. (2022). COMPARISON OF LINEAR REGRESSIONS AND NEURAL NETWORKS FOR FORECASTING COVID-19 RECOVERED CASES. Jurnal Riset Informatika, 4(3), 277-282. https://doi.org/10.34288/jri.v4i3.409
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