Forecasting of Covid-19 positive cases in Indonesia using long short-term memory (LSTM).

Content Provider	Europe PMC
Author	Sunjaya, Bryan Alfason Permai, Syarifah Diana Gunawan, Alexander Agung Santoso
Copyright Year	2022
Abstract	Since the emergence of Covid-19, the condition of Covid-19 has increased and decreased several times along with the emergence of new variants. Therefore, change occurs quickly and is extreme. If the positive cases of covid occur beyond medical capacity, there will be inequality. Therefore, it is important to predict the number of positive cases of covid to avoid this. The objective of this research is to predict the number of positive cases of Covid-19 in Indonesia using the ARIMA and LSTM methods. The two methods were compared to obtain the best method for predicting positive cases of Covid-19 in Indonesia. The data used in this research is the number of positive cases of Covid-19 in Indonesia from 2020 to 2022. Based on the results of ARIMA modeling, showed that the prediction results for the number of positive Covid -19 cases are still not good. This is because the ARIMA model produced does not meet the assumptions. Therefore, modeling was carried out using the LSTM method to get better predictions of the number of positive cases of Covid -19 in Indonesia. Based on the comparison results of the RMSE and MAPE values on the ARIMA and LSTM models, it showed that the LSTM model is better than ARIMA.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC9829418&blobtype=pdf
Journal	Procedia Computer Science [Procedia Comput Sci]
Volume Number	216
DOI	10.1016/j.procs.2022.12.125
PubMed Central reference number	PMC9829418
PubMed reference number	36643183
e-ISSN	18770509
Language	English
Publisher	The Author(s). Published by Elsevier B.V.
Publisher Date	2023-01-10
Access Restriction	Open
Rights License	Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. © 2022 The Author(s). Published by Elsevier B.V.
Subject Keyword	Covid-19 Time Series Analysis ARIMA LSTM
Content Type	Text
Resource Type	Article
Subject	Computer Science