Prediction of Increased Mobility of Yogyakarta Residents in Controlling the Spread of COVID-19 Cases Using the Neural Network Algorithm

Syaiful Rachmat Dana, Esi Putri Silmina

Abstract

High mobility in D.I Yogyakarta arises from tourist visits and educational activities as well as community activities which are quite dense. High mobility and density will affect the spread of COVID-19 in D.I Yogyakarta. Early preparation is needed to predict displacement in D.I Yogyakarta so that policies can be implemented as early as possible to prevent the emergence of a new wave of spread of COVID-19 in D.I Yogyakarta. In this study, the Neural Network Algorithm with Backpropagation model is used to predict the mobility of Yogyakarta D.I data from September 2021 to January 2022. To form the Neural Network Algorithm model using Rapidminer software with prediction criteria of accuracy and kappa. The prediction accuracy value obtained is 95.49% and the kappa value is 0.908. The results of this study indicate that the predictive value of Yogyakarta D.I mobility tends to be high.

Keywords

Backpropagation, forecasting, Mobility, Neural Network, Rapidminer

References

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