Prediction New Cases of COVID-19 in Indonesia Using Vector Autoregression (VAR) and Long-Short Term Memory (LSTM) Methods
2021 International Conference on Artificial Intelligence and Big Data Analytics, ICAIBDA 2021
; : 127-130, 2021.
Article
in English
| Scopus | ID: covidwho-1774626
ABSTRACT
The addition of Covid-19 cases is still uncontrolled, especially in Indonesia. Often the addition of Covid-19 cases in Indonesia always experiences a significant upward trend after a slightly loose government policy. This is because the government does not think there will be a spike in cases after cases go down. This is where the importance of predicting new cases of Covid-19 in Indonesia to be a reference for the government in taking policy. With deep learning, the prediction results will be more accurate. The implementation of vector autoregression (VAR) and long-short term memory (LSTM) methods can reach an accretion rate of up to 98%. With this method, the prediction results can be used for the government in anticipating if there is a surge in new cases per day because it has been predicted from the beginning. In fact, this method can predict new cases for up to a year. © 2021 IEEE.
Artificial Intelligence; Long-Short Term Memory (LSTM); New Cases; Prediction; Vector Autoregression (VAR); Brain; Long short-term memory; Regression analysis; Value engineering; Accretion rate; Indonesia; Long-short term memory; New case; Upward trend; Vector autoregression; Vector autoregressions; Forecasting
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Topics:
Long Covid
Language:
English
Journal:
2021 International Conference on Artificial Intelligence and Big Data Analytics, ICAIBDA 2021
Year:
2021
Document Type:
Article
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