How to cite this paper
Hidayat, Y., Pangestu, D., Subiyanto, S., Purwandari, T., Sukono, S & Saputra, J. (2022). Predicting the weekly COVID-19 new cases using multilayer perceptron: An evidence from west Java, Indonesia.Decision Science Letters , 11(3), 247-262.
Refrences
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Allen, T. (2010). Introduction to Engineering Statistics and Lean Sigma. London, UK: Springer-Verlag.
Bholowalia, P., & Kumar, A. (2014). EBK-Means: A Clustering Technique based on Elbow Method and K-Means in WSN. International Journal of Computer Applications, 105(9), 17–24.
Brauer, F. (2008). Compartmental models in epidemiology. In Mathematical epidemiology (pp. 19–79). Springer.
Cheng, B., & Titterington, D. M. (1994). Neural Networks: A Review from Statistical Perspective. Statistical Science, 9(1), 2–30.
Chintalapudi, N., Battineni, G., & Amenta, F. (2020). COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach. Journal of Microbiology, Immunology and Infection.
Cooper, I., Mondal, A., & Antonopoulos, C. G. (2020). A SIR model assumption for the spread of COVID-19 in different communities. Chaos, Solitons & Fractals, 139, 110057.
Das, G., Lin, K.-I., Mannila, H., Renganathan, G., & Smyth, P. (1998). Rule Discovery from Time Series. In KDD (Vol. 98, pp. 16–22).
Du, K. L., & Swamy, M. N. S. (2013). Neural Networks and Statistical Learning. London, UK: Springer.
Faruk, O., & Kar, S. (2021). A Data Driven Analysis and Forecast of COVID-19 Dynamics during the Third Wave Using SIRD Model in Bangladesh. COVID, 1(2), 503–517.
Fausett, L. V. (2006). Fundamentals of neural networks: architectures, algorithms and applications. Pearson Education India.
Forero, R., & Hillman, K. (2008). Access block and overcrowding: a literature review. Prepared for the Australasian College for Emergency Medicine. Sydney: University of New South Wales.
Garg, S., Kim, L., Whitaker, M., O’Halloran, A., Cummings, C., Holstein, R., … Alden, N. B. (2020). Hospitalization rates and characteristics of patients hospitalized with laboratory-confirmed coronavirus disease 2019—COVID-NET, 14 States, March 1–30, 2020. Morbidity and Mortality Weekly Report, 69(15), 458.
Gurney, K. (2004). Introduction to neural networks. London, UK: Taylor & Francis e-Library.
Hasan, A., & Nasution, Y. (2021). A compartmental epidemic model incorporating probable cases to model COVID-19 outbreak in regions with limited testing capacity. ISA Transactions.
He, W., Feng, G., Wu, Q., He, T., Wan, S., & Chou, J. (2012). A new method for abrupt dynamic change detection of correlated time series. International Journal of Climatology, 32(10), 1604–1614.
Kandıran, E., & Hacinliyan, A. (2019). Comparison of Feedforward and Recurrent Neural Network in Forecasting Chaotic Dynamical System. AJIT-e: Online Academic Journal of Information Technology, 10(37), 31–44.
Kaufman, L., & Rousseeuw, P. J. (1990). Finding Groups in data: An Introduction to Cluster Analysis. New York, NY, USA: John Wiley & Sons, Inc.
Kaufman, L., & Rousseeuw, P. J. (2005). Finding Groups in data: An Introduction to Cluster Analysis. New York, NY, USA: John Wiley & Sons, Inc.
Keogh, E., Lonardi, S., & Chiu, B. (2002). Finding surprising patterns in a time series database in linear time and space. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 550–556).
Kingma, D. P., & Ba, J. L. (2015). Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings (pp. 1–15). San Diego, CA, USA.
Kodinariya, T. M., & Makwana, P. R. (2013). Review on determining of cluster in K-means. International Journal of Advance Research in Computer Science and Management Studies, 1(6), 90–95.
Kumar, D., Malviya, R., & Sharma, P. K. (2020). Corona virus: a review of COVID-19. EJMO, 4(1), 8–25.
Liang, K. (2020). Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS. Infection, Genetics and Evolution, 82(April), 104306.
Liao, T. W. (2005). Clustering of time series data—a survey. Pattern Recognition, 38(11), 1857–1874.
Lin, J., Vlachos, M., Keogh, E., & Gunopulos, D. (2004). Iterative incremental clustering of time series. In International Conference on Extending Database Technology (pp. 106–122). Springer.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PloS One, 13(3), e0194889.
Manliura Datilo, P., Ismail, Z., & Dare, J. (2019). A Review of Epidemic Forecasting Using Artificial Neural Networks. International Journal of Epidemiologic Research, 6(3), 132–143.
Montgomery, D. C. (2019). Introduction to Statistical Quality Control (8th Editio). New York, NY, USA: John Wiley & Sons, Inc.
Panchal, F. S., & Panchal, M. (2014). International Journal of Computer Science and Mobile Computing Review on Methods of Selecting Number of Hidden Nodes in Artificial Neural Network. International Journal of Computer Science and Mobile Computing, 3(11), 455–464.
Pavlidis, N. G., Plagianakos, V. P., Tasoulis, D. K., & Vrahatis, M. N. (2006). Financial forecasting through unsupervised clustering and neural networks. Operational Research, 6(2), 103–127.
Pontoh, R. S., Toharudin, T., Zahroh, S., & Supartini, E. (2020). Effectiveness of the public health measures to prevent the spread of covid-19. Commun. Math. Biol. Neurosci., 2020, Article-ID.
Rani, S., & Sikka, G. (2012). Recent techniques of clustering of time series data: a survey. International Journal of Computer Applications, 52(15).
Roosa, K., Lee, Y., Luo, R., Kirpich, A., Rothenberg, R., Hyman, J. M., … Chowell, G. (2020). Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infectious Disease Modelling, 5, 256–263.
Rothe, C., Schunk, M., Sothmann, P., Bretzel, G., Froeschl, G., Wallrauch, C., … Guggemos, W. (2020). Transmission of 2019-nCoV infection from an asymptomatic contact in Germany. New England Journal of Medicine, 382(10), 970–971.
Sfetsos, A., & Siriopoulos, C. (2004). Time series forecasting with a hybrid clustering scheme and pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 34(3), 399–405.
Shahnawaz, M., Ranjan, A., & Danish, M. (2011). Temporal Data Mining: An Overview. International Journal of Engineering and Advanced Technology, 1(1), 20–24.
Shokoohi-Yekta, M., Chen, Y., Campana, B., Hu, B., Zakaria, J., & Keogh, E. (2015). Discovery of meaningful rules in time series. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1085–1094).
Subiyanto, Hidayat, Y., Afrianto, E., & Supian, S. (2021). Numerical Estimation of the Final Size of the Spread of COVID-19 in West Java Province , Indonesia. Engineering Letters, 29(3), 1015–1019.
Sujatha, R., Chatterjee, J. M., & Hassanien, A. E. (2020). Correction to: A machine learning forecasting model for COVID-19 pandemic in India. Stochastic Environmental Research and Risk Assessment, 34, 959–972.
Thorndike, R. L. (1953). Who belongs in the family? Psychometrika, 18(4), 267–276.
Tosepu, R., Effendy, D. S., Lestari, H., Bahar, H., Asfian, P., & Sakka, A. (2020). Vulnerability of weather on COVID-19 pandemic in West Java, Indonesia. Public Health of Indonesia, 6(4), 123–128.
West Java Provincial Government. (2021). Incidence Rate in West Java.
Wibowo, F. W. (2021). Prediction modelling of COVID-19 on provinces in Indonesia using long short-term memory machine learning. In Journal of Physics: Conference Series (Vol. 1844, p. 12006). IOP Publishing.
Worldometer. (2021). Covid-19 Coronavirus Pandemic.
Zulfikar, M. (2021). 19 received 34 reports of COVID-19 patients being rejected by the hospital.
Allen, T. (2010). Introduction to Engineering Statistics and Lean Sigma. London, UK: Springer-Verlag.
Bholowalia, P., & Kumar, A. (2014). EBK-Means: A Clustering Technique based on Elbow Method and K-Means in WSN. International Journal of Computer Applications, 105(9), 17–24.
Brauer, F. (2008). Compartmental models in epidemiology. In Mathematical epidemiology (pp. 19–79). Springer.
Cheng, B., & Titterington, D. M. (1994). Neural Networks: A Review from Statistical Perspective. Statistical Science, 9(1), 2–30.
Chintalapudi, N., Battineni, G., & Amenta, F. (2020). COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach. Journal of Microbiology, Immunology and Infection.
Cooper, I., Mondal, A., & Antonopoulos, C. G. (2020). A SIR model assumption for the spread of COVID-19 in different communities. Chaos, Solitons & Fractals, 139, 110057.
Das, G., Lin, K.-I., Mannila, H., Renganathan, G., & Smyth, P. (1998). Rule Discovery from Time Series. In KDD (Vol. 98, pp. 16–22).
Du, K. L., & Swamy, M. N. S. (2013). Neural Networks and Statistical Learning. London, UK: Springer.
Faruk, O., & Kar, S. (2021). A Data Driven Analysis and Forecast of COVID-19 Dynamics during the Third Wave Using SIRD Model in Bangladesh. COVID, 1(2), 503–517.
Fausett, L. V. (2006). Fundamentals of neural networks: architectures, algorithms and applications. Pearson Education India.
Forero, R., & Hillman, K. (2008). Access block and overcrowding: a literature review. Prepared for the Australasian College for Emergency Medicine. Sydney: University of New South Wales.
Garg, S., Kim, L., Whitaker, M., O’Halloran, A., Cummings, C., Holstein, R., … Alden, N. B. (2020). Hospitalization rates and characteristics of patients hospitalized with laboratory-confirmed coronavirus disease 2019—COVID-NET, 14 States, March 1–30, 2020. Morbidity and Mortality Weekly Report, 69(15), 458.
Gurney, K. (2004). Introduction to neural networks. London, UK: Taylor & Francis e-Library.
Hasan, A., & Nasution, Y. (2021). A compartmental epidemic model incorporating probable cases to model COVID-19 outbreak in regions with limited testing capacity. ISA Transactions.
He, W., Feng, G., Wu, Q., He, T., Wan, S., & Chou, J. (2012). A new method for abrupt dynamic change detection of correlated time series. International Journal of Climatology, 32(10), 1604–1614.
Kandıran, E., & Hacinliyan, A. (2019). Comparison of Feedforward and Recurrent Neural Network in Forecasting Chaotic Dynamical System. AJIT-e: Online Academic Journal of Information Technology, 10(37), 31–44.
Kaufman, L., & Rousseeuw, P. J. (1990). Finding Groups in data: An Introduction to Cluster Analysis. New York, NY, USA: John Wiley & Sons, Inc.
Kaufman, L., & Rousseeuw, P. J. (2005). Finding Groups in data: An Introduction to Cluster Analysis. New York, NY, USA: John Wiley & Sons, Inc.
Keogh, E., Lonardi, S., & Chiu, B. (2002). Finding surprising patterns in a time series database in linear time and space. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 550–556).
Kingma, D. P., & Ba, J. L. (2015). Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings (pp. 1–15). San Diego, CA, USA.
Kodinariya, T. M., & Makwana, P. R. (2013). Review on determining of cluster in K-means. International Journal of Advance Research in Computer Science and Management Studies, 1(6), 90–95.
Kumar, D., Malviya, R., & Sharma, P. K. (2020). Corona virus: a review of COVID-19. EJMO, 4(1), 8–25.
Liang, K. (2020). Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS. Infection, Genetics and Evolution, 82(April), 104306.
Liao, T. W. (2005). Clustering of time series data—a survey. Pattern Recognition, 38(11), 1857–1874.
Lin, J., Vlachos, M., Keogh, E., & Gunopulos, D. (2004). Iterative incremental clustering of time series. In International Conference on Extending Database Technology (pp. 106–122). Springer.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PloS One, 13(3), e0194889.
Manliura Datilo, P., Ismail, Z., & Dare, J. (2019). A Review of Epidemic Forecasting Using Artificial Neural Networks. International Journal of Epidemiologic Research, 6(3), 132–143.
Montgomery, D. C. (2019). Introduction to Statistical Quality Control (8th Editio). New York, NY, USA: John Wiley & Sons, Inc.
Panchal, F. S., & Panchal, M. (2014). International Journal of Computer Science and Mobile Computing Review on Methods of Selecting Number of Hidden Nodes in Artificial Neural Network. International Journal of Computer Science and Mobile Computing, 3(11), 455–464.
Pavlidis, N. G., Plagianakos, V. P., Tasoulis, D. K., & Vrahatis, M. N. (2006). Financial forecasting through unsupervised clustering and neural networks. Operational Research, 6(2), 103–127.
Pontoh, R. S., Toharudin, T., Zahroh, S., & Supartini, E. (2020). Effectiveness of the public health measures to prevent the spread of covid-19. Commun. Math. Biol. Neurosci., 2020, Article-ID.
Rani, S., & Sikka, G. (2012). Recent techniques of clustering of time series data: a survey. International Journal of Computer Applications, 52(15).
Roosa, K., Lee, Y., Luo, R., Kirpich, A., Rothenberg, R., Hyman, J. M., … Chowell, G. (2020). Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infectious Disease Modelling, 5, 256–263.
Rothe, C., Schunk, M., Sothmann, P., Bretzel, G., Froeschl, G., Wallrauch, C., … Guggemos, W. (2020). Transmission of 2019-nCoV infection from an asymptomatic contact in Germany. New England Journal of Medicine, 382(10), 970–971.
Sfetsos, A., & Siriopoulos, C. (2004). Time series forecasting with a hybrid clustering scheme and pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 34(3), 399–405.
Shahnawaz, M., Ranjan, A., & Danish, M. (2011). Temporal Data Mining: An Overview. International Journal of Engineering and Advanced Technology, 1(1), 20–24.
Shokoohi-Yekta, M., Chen, Y., Campana, B., Hu, B., Zakaria, J., & Keogh, E. (2015). Discovery of meaningful rules in time series. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1085–1094).
Subiyanto, Hidayat, Y., Afrianto, E., & Supian, S. (2021). Numerical Estimation of the Final Size of the Spread of COVID-19 in West Java Province , Indonesia. Engineering Letters, 29(3), 1015–1019.
Sujatha, R., Chatterjee, J. M., & Hassanien, A. E. (2020). Correction to: A machine learning forecasting model for COVID-19 pandemic in India. Stochastic Environmental Research and Risk Assessment, 34, 959–972.
Thorndike, R. L. (1953). Who belongs in the family? Psychometrika, 18(4), 267–276.
Tosepu, R., Effendy, D. S., Lestari, H., Bahar, H., Asfian, P., & Sakka, A. (2020). Vulnerability of weather on COVID-19 pandemic in West Java, Indonesia. Public Health of Indonesia, 6(4), 123–128.
West Java Provincial Government. (2021). Incidence Rate in West Java.
Wibowo, F. W. (2021). Prediction modelling of COVID-19 on provinces in Indonesia using long short-term memory machine learning. In Journal of Physics: Conference Series (Vol. 1844, p. 12006). IOP Publishing.
Worldometer. (2021). Covid-19 Coronavirus Pandemic.
Zulfikar, M. (2021). 19 received 34 reports of COVID-19 patients being rejected by the hospital.