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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Risky Saputra Siahaan
Institut Teknologi Del

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Evaluating the efficacy of univariate LSTM approach for COVID-19 data prediction in Indonesia Tegar Arifin Prasetyo; Joshua Pratama Silitonga; Matthew Alfredo; Risky Saputra Siahaan; Roberd Saragih; Dewi Handayani; Rudy Chandra
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1353-1366

Abstract

The coronavirus disease 2019 (COVID-19) pandemic, originating in 2020, has emerged as a critical global issue due to its rapid and widespread transmission. Indonesia, among the affected nations, has taken measures to address the situation, including the development of a deep learning model for predicting future COVID-19 infection and spread. This predictive tool serves as a valuable reference for the government and stakeholders, aiding them in making informed decisions and implementing appropriate measures to contain the virus. The deep learning model employs the long short-term memory (LSTM) algorithm, chosen for its ability to recognize temporal patterns in the country’s COVID-19 data. The model creation process involves data collection, preprocessing, model architecture planning, modeling, training, and evaluation. Two LSTM models were developed: a univariate and a multivariate model. Following thorough training and evaluation, the univariate model emerged as the superior choice, boasting evaluation metrics of 16.72 for mean absolute percentage error (MAPE) and 66.36 for root mean squared error (RMSE). This model was then deployed on a publicly accessible website, presenting visualizations of past COVID-19 data and predictions of future cases through line graphs. This user-friendly platform enables the public to access and analyze the data easily.
Co-Authors Dewi Handayani Joshua Pratama Silitonga Matthew Alfredo Prasetyo, Tegar Arifin Roberd Saragih Rudy Chandra