Evidence map›Paper›PMID 36643183›Full record

ArticleProcedia computer science2023

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

Bryan Alfason Sunjaya, Syarifah Diana Permai, Alexander Agung Santoso Gunawan

Abstract read
In one paragraph

Article in Procedia computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Bryan Alfason SunjayaStatistics Department, School of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia.
Syarifah Diana PermaiStatistics Department, School of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia.
Alexander Agung Santoso GunawanComputer Science Department, School of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

ARIMACovid-19LSTMTime Series Analysis

Identifiers

PMID36643183
PMCPMC9829418

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.