Predicting the Spread of the Corona Virus (COVID-19) in Indonesia: Approach Visual Data Analysis and Prophet Forecasting
International Journal of artificial intelligence research
View Publication Info| Field | Value | |
| Title |
Predicting the Spread of the Corona Virus (COVID-19) in Indonesia: Approach Visual Data Analysis and Prophet Forecasting
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| Creator |
Husein, Amir Mahmud
Hutabarat, Jefri Poltak Sitorus, Jeckson Edition Giawa, Tonazisokhi Harahap, Mawaddah |
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| Subject |
Artificial Intelligence; Science; Machine Learning
Exploratory Data Analysis; Prophet; Coronavirus; COVID-19; Forecasting |
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| Description |
The development trend of the coronavirus pandemic (COVID-19) in various countries has become a global threat, including in Southeast Asia, such as Indonesia, the Philippines, Brunei, Malaysia, and Singapore. In this paper, we propose an Exploratory Data Analysis (EDA) model approach and a time series forecasting model using the Prophet method to predict the number of confirmed cases and cases of death in Indonesia in the next thirty days. We apply the EDA model to visualize and provide an understanding of this pandemic outbreak in various countries, especially in Indonesia. We present the trends in the spread of epidemics from the countries of China from which the virus originates, then mark the top ten countries and their development and also present the trends in Asian countries. We present an analytical framework comparing the predicted results with the actual data evaluated using the MAPE and MAE models, where the prophet algorithm produces good performance based on the evaluation results, the relative error rate of our estimate (MAPE) is around 6.52%, and the model average false 52.7% (MAE) for confirmed cases, while case mortality was 1.3% for the MAPE and MAE models around 236.6%. The results of the analysis can be used as a reference for the Indonesian government in making decisions to prevent its spread in order to avoid an increase in the number of deaths
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| Publisher |
STMIK Dharma Wacana
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| Contributor |
—
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| Date |
2020-12-21
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| Type |
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion Peer-reviewed Article |
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| Format |
application/pdf
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| Identifier |
http://ijair.id/index.php/ijair/article/view/192
10.29099/ijair.v5i1.192 |
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| Source |
International Journal of Artificial Intelligence Research; Vol 4, No 2 (2020): December; 151 - 161
2579-7298 10.29099/ijair.v4i2 |
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| Language |
eng
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| Relation |
http://ijair.id/index.php/ijair/article/view/192/pdf
http://ijair.id/index.php/ijair/article/downloadSuppFile/192/44 http://ijair.id/index.php/ijair/article/downloadSuppFile/192/45 http://ijair.id/index.php/ijair/article/downloadSuppFile/192/46 |
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| Rights |
Copyright (c) 2021 International Journal of Artificial Intelligence Research
https://creativecommons.org/licenses/by-sa/4.0 |
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