Prediction Modelling of COVID-19 on Provinces in Indonesia using Long Short-Term Memory Machine Learning

Abstract

The COVID-19 is a dangerous virus that has been declared by the world health organization (WHO) as a pandemic. Many countries have taken policies to control the virus's spread and have played an active role in overcoming this global pandemic, including Indonesia. Indonesia consists of many islands, so the level of distribution varies. Although the mortality rate is shallow than the cure rate, this virus's spread must be controlled. This paper aims to model the prediction of infected cases, cases of recovery from COVID-19, and mortality for each province in Indonesia using the Long Short-Term Memory (LSTM) machine learning method. The results of the model evaluation of this method used the root mean squared error (RMSE) approach.