Prediction Modelling of COVID-19 on Provinces in Indonesia using Long Short-Term Memory Machine Learning

Wibowo, Ferry Wahyu; Wihayati,

doi:10.1088/1742-6596/1844/1/012006

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…The higher the resulting RMSE value, the lower the level of accuracy, and the lower the resulting RMSE value, the higher the level of accuracy [5]. The second measure is the Mean Absolute Percentage Error (MAPE) calculation, to determine LSTM performance evaluation in forecasting (forecasting), which can be seen in the following equation (2). The use of MAPE in the evaluation of the prediction results can calculate the measurement accuracy of the actual value and the predicted value.…”

Section: G Evaluation Of Test Resultsmentioning

confidence: 99%

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Forecasting Covid-19 Time Series Data using the Long Short-Term Memory (LSTM)

Mukhtar¹,

Taufiq²,

Herwinanda³

et al. 2022

IJACSA

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Confirmed statistical data of Covid-19 cases that have accumulated sourced from (https://corona.riau.go.id/datastatistik/) in Riau Province on June 7, 2021, there were 63441 cases, on June 14, 2021, it increased to 65883 cases, on June 21, 2021, it increased to 67910, and on June 28, 2021, it increased to 69830 cases. Since the beginning of this pandemic outbreak, it has been observed that the case data continues to increase every week until this July. This study predicts cases of Covid-19 time series data in Riau Province using the LSTM algorithm, with a dataset of 64 lines. Long-Short Term Memory has the ability to store memory information for patterns in the data for a long time at the same time. Tests predicting historical data for Covid-19 cases in Riau Province resulted in the lowest RMSE value in the training data, which was 8.87, and the test data, which was 13.00, in the death column. The evaluation of the best MAPE value in the training data, which is 0.23%, is in the recovered column, and the evaluation of the best MAPE value in the test data, which is 0.27%, in the positive_number column. In the test to predict the next 30 days using the LSTM model that has been trained, it was found that the performance evaluation of the prediction results for the positive_number column and the death column was very good, the recovery column was categorized as good, the independent_isolation column and the care_rs column were categorized as poor.

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Section: G Evaluation Of Test Resultsmentioning

confidence: 99%

“…The Covid-19 pandemic entered Indonesia on March 2, 2020, it is known from existing information that there have been Indonesian citizens who have been infected. The two 64-year-old women, and their 31-year-old daughter were declared positive for Covid-19 after having contact with a Japanese citizen [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Covid-19 Time Series Data using the Long Short-Term Memory (LSTM)

Mukhtar¹,

Taufiq²,

Herwinanda³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…The Indonesian government is more alert to the spread of COVID-19, namely by issuing a lockdown policy to PSBB (Large-Scale Social Restrictions). [11]. Almost all Indonesians have complied with the guidelines set by the government, namely carrying out all activities in a WFH manner to minimize the chain of distribution of COVID-19 in Indonesia.…”

Section: Introductionmentioning

confidence: 99%

“…The year 2019 (COVID-19) [13]. The Indonesian government has recorded 14,032 confirmed COVID-19 positives as of May 10, 2020 [11]. Nine hundred seventy-three people have died from COVID-19, while 2,698 people have beaten the condition.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the recovery of COVID-19 patients in East Java using the Fuzzy time series Cheng method

Yulianto

Faisol

Zahroh

et al. 2021

J. Nat. Scien. & Math. Res.

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Coronavirus 2019 (COVID-19) has significantly impacted Indonesia. Social restrictions in Indonesia's major cities and rural areas have been put in place as the coronavirus spreads. The Indonesian government is more vigilant with the spread of COVID-19, namely by issuing a lockdown policy to PSBB (Large-Scale Social Restrictions). Almost all Indonesian people have complied with the guidelines set by the government, namely carrying out all activities in a WFH manner to minimize the chain of distribution of COVID-19 in Indonesia. The author of this work forecasts the recovery rate of Covid-19 patients in the East Java region using the Cheng Fuzzy Time Series approach. After checking the simulation with real in the field, it can be seen that using 51 data starting from February 4 2021 to March 26 2021 gives results MAPE = 0.4602%, which means the forecasting is very accurate.©2021 JNSMR UIN Walisongo. All rights reserved.

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Prediction of air quality in Jakarta during the COVID-19 outbreak using long short-term memory machine learning

Wihayati¹,

Wibowo²

2021

IOP Conf. Ser.: Earth Environ. Sci.

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Air pollution is one of the world’s problems, not just one location. This air pollution is caused by pollutants that are harmful to human health and the environment. Some pollutants are most influential, namely particulate matter, ground-level ozone, carbon monoxide, sulfur dioxide, and nitrogen dioxide. Several countries decided to lock down when the COVID-19 outbreak was announced simultaneously throughout the world like a pandemic. In Jakarta, Indonesia applies large-scale social restrictions (PSBB). The resulting impact is a drastic reduction in air pollution on air quality. This paper aims to predict air quality during the COVID-19 outbreak in Jakarta using long short-term memory (LSTM) machine learning. The evaluation of the LSTM model used in this paper is the root mean square error (RMSE). The results obtained show that the Adam optimizer can bring the prediction results closer to the dataset used.

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Prediction Modelling of COVID-19 on Provinces in Indonesia using Long Short-Term Memory Machine Learning

Cited by 5 publications

References 7 publications

Forecasting Covid-19 Time Series Data using the Long Short-Term Memory (LSTM)

Forecasting Covid-19 Time Series Data using the Long Short-Term Memory (LSTM)

Forecasting the recovery of COVID-19 patients in East Java using the Fuzzy time series Cheng method

Prediction of air quality in Jakarta during the COVID-19 outbreak using long short-term memory machine learning

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