Prediction Active Case of Covid-19 with ERNN

Authors

  • Winda Aprianti Information Technology, Politeknik Negeri Tanah Laut http://orcid.org/0000-0001-7980-6426
  • Jaka Permadi Information Technology, Politeknik Negeri Tanah Laut
  • Herfia Rhomadhona Information Technology, Politeknik Negeri Tanah Laut

DOI:

https://doi.org/10.31764/jtam.v6i1.4874

Keywords:

Active Case, Covid-19 Pandemic, Neural Network, ERNN Algorithm,

Abstract

SARS-CoV-2 is known as Covid-19 has been spread in all world since end of 2019. Indonesia, including South Kalimantan has detected first Covid-19 in March 2020. This pandemic has affected in all entirely live in Indonesia. This makes Covid-19 be the main focus of the government. The government has provided aid and imposed restrictions on activities. These policies require planning that can be a solution. Careful planning requires an overview of the data on active cases that are positive for Covid-19. This overview can be obtained through prediction. In this research, Elman Recurrent Neural Network (ERNN) was used to predict active cases of Covid-19. Architecture of ERNN was used ERNN with 3 input nodes, 2 hidden nodes, and 2 context nodes. The data used is 277 data, which is then divided into training data and testing data, respectively 90%-10%, 80%-20%, and 70%-30%. ERNN with a learning rate of 0.1 until 0.9 is applied to data on active cases of Covid-19, then Mean Absolute Percentage Error (MAPE) is calculated to find out performance of model generated by ERNN. The results showed that all of MAPE were below 10% with the smallest MAPE as 3.21% for scenario 90:10 and learning rate 0.6. MAPE value which is less than 10% indicates that ERNN has very good predictive ability.

 

References

Abdul, M., & Ashour, H. (2018). Improving Time Series ’Forecast Errors by Using Recurrent Neural Networks. ICSCA 2018, (February), 229–232. https://doi.org/10.1145/3185089.3185151

Altelbany, S. I., & Abualhussein, A. A. (2021). Performance Comparison of Neural Networks ( MLP , RBFNN , ERNN , JRNN ) Models for Stock Prices Forecasting to Bank of Palestine. Muthanna Journal of Administrative and Economic Sciences, 11(1), 8–28. https://doi.org/10.52113/6/2021-11/8-28

Aprianti, W., Permadi, J., & Rhomadhona, H. (2020). Perbandingan Elman Recurrent Neural Networks, Backpropagation Neural Networks, dan Exponential Smoothing dalam Peramalan Produksi Palawija. MUST: Journal of Mathematics Education, Science and Technology, 5(2), 206–220. https://doi.org/http://doi.org/10.30651/must.v5i2.6255

Astuti, T., & Pratika, I. (2019). Product Review Sentiment Analysis by Artificial Neural Network Algorithm. International Journal of Informatics and Information Systems, 2(2), 61–66.

Berradi, Z., Lazaar, M., Omara, H., & Mahboub, O. (2020). Effect of Architecture in Recurrent Neural Network Applied on the Prediction of Stock Price. IAENG International Journal of Computer Science, 47(3), 199–204.

Chen, Y. (2018). Applications of Recurrent Neural Networks in Environmental Factor Forecasting : A Review. Neural Computation, 30, 2855–2881. https://doi.org/10.1162/neco_a_01134

Choubin, B., Khalighi-Sigaroodi, S., Malekian, A., & Kişi, Ö. (2016). Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals. Hydrological Sciences Journal, 61(6), 1001–1009. https://doi.org/10.1080/02626667.2014.966721

Dada, E. G., Yakubu, H. J., & Oyewola, D. O. (2021). Artificial Neural Network Models for Rainfall Prediction. EJECE, European Journal of Electrical Engineering and Computer Science, 5(2), 30–35. https://doi.org/http://dx.doi.org/10.24018/ejece.2021.5.2.313

Devarajan, S., & S, C. (2019). Load Forecasting Model for Energy Management System Using Elman Neural Network. International Research Journal of Multidisciplinary Technovation (IRJMT), 1(3), 48–56. https://doi.org/https://doi.org/10.34256/irjmt1936

Dinzi, R., & Energy, A. W. (2020). The Use of Meteorology Data in Short-Term Prediction of Wind Speed for Wind Turbine Using Elman Recurrent Neural Network. 2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA), 93–98.

Farhana, N., & Sophiayati, S. (2015). Forecasting Rainfall Distribution Using Artificial Neural Networks for Johor Rivers. Open International Journal of Informatics (OIJI), 3(1), 11–27.

Ismael, L., & Ismael, A. I. (2019). Prediction the data consumption for power demands by Elman neural network. International Journal of Electrical and Computer Engineering (IJECE), 9(5), 4003–4009. https://doi.org/10.11591/ijece.v9i5.pp4003-4009

Khaldi, R., & Afia, A. El. (2018). Feedforward and Recurrent Neural Networks for Time Series Forecasting : Comparative Study. Proceedings of ACM LOPAL Conference, 1–6. https://doi.org/https://doi.org/10.1145/3230905.3230946

Krichene, E., Masmoudi, Y., & Alimi, A. M. (2017). Forecasting Using Elman Recurrent Neural Network. Intelligent Systems Design and Applications, 2(ii), 488–497. https://doi.org/10.1007/978-3-319-53480-0

Li, J., Zhang, B., & Mao, C. (2011). Wind speed prediction and error distribution based on rational sample organisation for Elman recursion neural networks. International Journal Advanced Mechatronic Systems, 3(July 2010), 17–19.

Madadipouya, K. (2017). A survey on data mining algorithms and techniques in medicine. International Journal on Informatics Visualization, 1(3), 61–71. https://doi.org/10.30630/joiv.1.3.25

Pal, A., Singh, J. P., & Dutta, P. (2015). Path length prediction in MANET under AODV routing: Comparative analysis of ARIMA and MLP model. Egyptian Informatics Journal, 16(1), 103–111. https://doi.org/10.1016/j.eij.2015.01.001

Prasetiyo, B., Hakim, M. F. Al, & Pradana, F. D. (2021). Prediction of COVID-19 Using Recurrent Neural Network Model. Scientific Journal of Informatics, 8(1), 98–103. https://doi.org/10.15294/sji.v8i1.30070

Stepchenko, A., & Chizhov, J. (2015). NDVI Short-Term Forecasting Using Recurrent Neural Networks. Proceedings of the 10th International Scientific and Practical Conference, 3, 180–185. https://doi.org/http://dx.doi.org/10.17770/etr2015vol3.167

Sugiartawan, P., & Hartati, S. (2019). Time Series Data Prediction using Elman Recurrent Neural Network on Tourist Visits in Tanah Lot Tourism Object. International Journal of Engineering and Advanced Technology (IJEAT), (1), 314–320. https://doi.org/10.35940/ijeat.A1833.109119

Vaziri, N., Erfani, A., & Nilforooshan, B. (2012). Signal Prediction in the LOCA Using Elman Recurrent Neural Networks. International Journal of Science and Engineering Investigations, 1(7), 1–4.

Wang, J., Wang, J., Fang, W., & Niu, H. (2016). Financial Time Series Prediction Using Elman Recurrent Random Neural Networks. Computational Intelligence and Neuroscience, 2016, 1–14.

Widiastuti, N. I., & Ali, M. I. (2021). Elman Recurrent Neural Network For Aspect Based Sentiment Analysis. Journal of Engineering Science and Technology, 16(3), 1991–2000.

Wutsqa, D. U., Kusumawati, R., & Subekti, R. (2014). The Application of Elman Recurrent Neural Network Model for Forecasting Consumer Price Index of Education, Recreation and Sports in Yogyakarta. 2014 10th International Conference on Natural Computation, 192–196.

Zheng, J. (2015). Forecast of Opening Stock Price Based on Elman Neural Network. The Chemical Engineering Transaction, 46(2014), 565–570. https://doi.org/10.3303/CET1546095

Downloads

Published

2022-01-22

Issue

Vol. 6 No. 1 (2022): January

Section

Articles

License

Authors who publish articles in JTAM (Jurnal Teori dan Aplikasi Matematika) agree to the following terms:

  1. Authors retain copyright of the article and grant the journal right of first publication with the work simultaneously licensed under a CC-BY-SA or The Creative Commons Attribution–ShareAlike License.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).