Implementation of Deep Learning Using Matlab-Based Convolutional Neural Network for Covid-19 Forecasting and Classification

Hendrik, Billy and Fauziah, Fauziah (2021) Implementation of Deep Learning Using Matlab-Based Convolutional Neural Network for Covid-19 Forecasting and Classification. IEEE Conference Number.

Text (Proceeding International) Implementation_of_Deep_Learning_Using_Matlab-Based_Convolutional_Neural_Network_for_Covid-19_Forecasting_and_Classification.pdf Download (10MB)

Abstract

Abstract— The outbreak of the Corona Virus Disease or better known as the Korona virus or Covid-19 was first detected to appear in China precisely in China's Wuhan city at the end of 2019, suddenly becoming a terrible terror for the world community, especially after taking the lives of hundreds of people in a relatively short time. Almost approximately 200 countries in the world infected with Corona viruses including Indonesia, the number of virus infection status known as Garry-19 is increasing there are cases that are easy to do forecasting and some are difficult to predict, forecasting process and classification depends on the following that is related to the related factors, mathematical model to be used and the existence of the data owned. In this study can be produced percentage accuracy of the training data for classification with CNN method of 89.79% and for predictions of 90.47% for the type of positive cases Garry with the output data of emergency status with 3 status i.e. transition, standby and responsiveness. Keywords— classification, covid-19, CNN, prediction

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