Model Jaringan Syaraf Tiruan dalam Peramalan Kasus Positif Covid-19 di Indonesia

Abstract

Mitigation steps to control Covid-19 outbreak in Indonesia need to take. One of those step is forecasting the spread of the disease. This study compare two artificial neural network models in catching the pattern of Covid-19 positive total cases in Indonesia. Data Training used in this study is Indonesian total positive cases of Covid-19 from March 2 until May 26. The next 10 days data become data testing to show the model accuracy in predicting Covid-19 total cases. MLP shows a better prediction comparing to ELM.Three different prediction accuracy measurement is used – MAE, MAPE, and RMSE. All of them shows less value in MLP than in ELM.

Implementation of Autoregresssive Integrated Moving Average (ARIMA) on Forecasting the Number of Positive Cases of Covid-19 in North Sumatra on September 2021

“…Forecasting the number of positive COVID-19 cases in various regions with different methods has been carried out, such as forecasting the number of active Covid-19 cases in West Java using the Multilayer Perceptron Feed Forward Neural Networks (Pangestu and Hidayat, 2021), forecasting positive cases of Covid-19 in Indonesia by using artificial neural networks [2]. In this study, to predict the number of positive cases of Covid-19, the time series forecasting method that can be used is ARIMA (Autoregressive Integrated Moving Average).…”