Prediction of Covid-19 Daily Case in Indonesia Using Long Short Term Memory Method
DOI:
https://doi.org/10.34148/teknika.v10i1.328Keywords:
Covid-19, machine learning, deep learning, LSTM, MSEAbstract
The impact of this pandemic affects various sectors in Indonesia, especially in the economic sector, due to the large-scale social restrictions policy to suppress this case's growth. The details of the growth of Covid-19 in Indonesia are still fluctuating and cannot be fully understood. Recently it has been developed by researchers related to the prediction of Covid-19 cases in various countries. One of them is using a machine learning technique approach to predict cases of daily increase Covid-19. However, the use of machine learning techniques results in the MSE error value in the thousands. This high number indicates that the prediction data using the model is still a high error rate compared to the actual data. In this study, we propose a deep learning approach using the Long Short Term Memory (LSTM) method to build a prediction model for the daily increase cases of Covid-19. This study's LSTM model architecture uses the LSTM layer, Dropout layer, Dense, and Linear Activation Function. Based on various hyperparameter experiments, using the number of neurons 10, batch size 32, and epochs 50, the MSE values were 0.0308, RMSE 0.1758, and MAE 0.13. These results prove that the deep learning approach produces a smaller error value than machine learning techniques, even closer to zero.
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References
Pawar, M. (2020). The Global Impact of and Responses to the COVID-19 Pandemic. The International Journal of Community and Social Development, Vol. 2(2), pp. 111—120. https://doi.org/10.1177/2516602620938542.
Pan, L., Mu, M., Yang, P., Sun, Y., Wang, R., Yan, J., Li, P., Hu, B., Wang, J., Hu, C., Jin, Y., Niu, X., Ping, R., Du, Y., Li, T., Xu, G., Hu, Q. & Tu, L. (2020). Clinical Characteristics of COVID-19 Patients With Digestive Symptoms in Hubei, China. The American Journal of Gastroenterology, Vol. 115(5), pp. 766—773. https://doi.org/10.14309/ajg.0000000000000620.
Johns Hopkins University. (2021). New Cases of COVID-19 In World Countries - Johns Hopkins Coronavirus Resource Center. Johns Hopkins University. https://coronavirus.jhu.edu/data/new-cases.
Mandayam, A.U., C, R. A., Siddesha, S. & Niranjan, S.K. (2020). Prediction of Covid-19 Pandemic Based on Regression. Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp. 1—5.
Gambhir, E., Jain, R., Gupta, A. & Tomer, U. (2020). Regression Analysis of COVID-19 Using Machine Learning Algorithms. Proceedings of the International Conference on Smart Electronics and Communication (ICOSEC 2020), pp. 65—71. https://doi.org/10.1201/9781351073974.
Jarndal, A., Husain, S., Zaatar, O., Gumaei, T.A. & Hamadeh, A. (2020). GPR and ANN based Prediction Models for COVID-19 Death Cases. 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), pp. 1—5. https://doi.org/10.1109/ccci49893.2020.9256564.
Sulasikin, A., Nugraha, Y., Kanggrawan, J. & Suherman, A.L. (2020). Forecasting for a Data-Driven Policy Using Time Series Methods in Handling COVID-19 Pandemic in Jakarta. 2020 IEEE International Smart Cities Conference (ISC2), pp. 1—6. https://doi.org/10.2139/ssrn.3714105.
Qian, F. & Chen, X. (2019). Stock Prediction Based on LSTM Under Different Stability. 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019, pp. 483—486. https://doi.org/10.1109/ICCCBDA.2019.8725709.
Chen, Z., Liu, Y. & Liu, S. (2017). Mechanical State Prediction Based on LSTM Neural Network. Chinese Control Conference (CCC), pp. 3876—3881. https://doi.org/10.23919/ChiCC.2017.8027963.
Wang, Y., Zhou, J., Chen, K., Wang, Y. & Liu, L. (2017). Water Quality Prediction Method Based on LSTM Neural Network. Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017, pp. 1—5. https://doi.org/10.1109/ISKE.2017.8258814.
Bodapati, S., Bandarupally, H. & Trupthi, M. (2020). COVID-19 Time Series Forecasting of Daily Cases, Deaths Caused and Recovered Cases using Long Short Term Memory Networks. 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), pp. 525—530. https://doi.org/10.1109/iccca49541.2020.9250863.
Kawal Covid 19. (2020). Informasi Terkini COVID-19 di Indonesia. Diakses dari: https://kawalcovid19.id/.
Zhang, Y., Hutchinson, P., Lieven, N.A.J. & Nunez-Yanez, J. (2020). Remaining Useful Life Estimation Using Long Short-Term Memory Neural Networks and Deep Fusion. IEEE Access, Vol. 8, pp. 19033—19045. https://doi.org/10.1109/ACCESS.2020.2966827.
Vinayakumar, R., Soman, K.P. & Poornachandran, P. (2017). Long Short-Term Memory Based Operation Log Anomaly Detection. 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) 2017, pp. 236—242. https://doi.org/10.1109/ICACCI.2017.8125846.
Le, X.H., Ho, H.V., Lee, G. & Jung, S. (2019). Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting. Water (Switzerland), Vol. 11(7). https://doi.org/10.3390/w11071387.
Alzahrani, S.I., Aljamaan, I.A., & Al-Fakih, E.A. (2020). Forecasting the Spread of the COVID-19 Pandemic in Saudi Arabia Using ARIMA Prediction Model Under Current Public Health Interventions. Journal of Infection and Public Health, Vol. 13(7), pp. 914—919. https://doi.org/10.1016/j.jiph.2020.06.001.
Rustam, F., Reshi, A.A., Mehmood, A., Ullah, S., On, B.W., Aslam, W. & Choi, G.S. (2020). COVID-19 Future Forecasting Using Supervised Machine Learning Models. IEEE Access, Vol. 8, pp. 101489—101499. https://doi.org/10.1109/ACCESS.2020.2997311.
