Prediction Active Case of Covid-19 with ERNN

Winda Aprianti; Jaka Permadi; Herfia Rhomadhona

doi:10.31764/jtam.v6i1.4874

Prediction Active Case of Covid-19 with ERNN

Winda Aprianti, Jaka Permadi, Herfia Rhomadhona

Abstract

SARS-CoV-2 is known as Covid-19 has been spread in all world since end of 2019. Indonesia, including South Kalimantan has detected first Covid-19 in March 2020. This pandemic has affected in all entirely live in Indonesia. This makes Covid-19 be the main focus of the government. The government has provided aid and imposed restrictions on activities. These policies require planning that can be a solution. Careful planning requires an overview of the data on active cases that are positive for Covid-19. This overview can be obtained through prediction. In this research, Elman Recurrent Neural Network (ERNN) was used to predict active cases of Covid-19. Architecture of ERNN was used ERNN with 3 input nodes, 2 hidden nodes, and 2 context nodes. The data used is 277 data, which is then divided into training data and testing data, respectively 90%-10%, 80%-20%, and 70%-30%. ERNN with a learning rate of 0.1 until 0.9 is applied to data on active cases of Covid-19, then Mean Absolute Percentage Error (MAPE) is calculated to find out performance of model generated by ERNN. The results showed that all of MAPE were below 10% with the smallest MAPE as 3.21% for scenario 90:10 and learning rate 0.6. MAPE value which is less than 10% indicates that ERNN has very good predictive ability.

Keywords

Active Case; Covid-19 Pandemic; Neural Network; ERNN Algorithm;

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DOI: https://doi.org/10.31764/jtam.v6i1.4874