Forecasting COVID-19 Total Daily Cases in Indonesia Using LSTM Networks

Clarissa Angelita Indriyani; Claudia Rachel Wijaya; Nunung Nurul Qomariyah

The COVID-19 virus has taken over the course of the world for over two years; governments all over the world have been trying to mitigate its effects in several ways such as instilling most jobs to be done at home instead of working from the office. Thus, it is important to be able to see predictions of COVID-19 cases to better plan the intervention of the virus spreading. With the use of machine learning, our paper aims to propose and evaluate an LSTM (Long Short Term Memory) model that can forecast daily COVID-19 cases in Indonesia. Several tests show that 50 epochs and a batch size of eight are the best parameters to use for our model. Furthermore, after comparison with differing amounts of lookbacks, we have decided that 10 is best for our model as it consistently does better than other numbers of lookbacks. Based on our model, there will still be an increase of COVID-19 cases in the future.