PREDIKSI PANDEMI COVID 19 KOTA SEMARANG MENGGUNAKAN PENDEKATAN NEURAL NETWORK

Aria Hendrawan, Vensy Vydia, Saifur Rohman Cholil

Abstract

Pandemi COVID-19 (Coronavirus) cenderung menjadi salah satu masalah paling serius global dalam satu tahun terakhir ini. Negara tidak memiliki pengalaman serupa terkait penyebaran virus dan dampaknya dari berbagai bidang. Memperkirakan jumlah kasus COVID-19 sebelumnya dapat membantu dalam mengambil keputusan berupa tindakan dan rencana pencegahan virus tersebut. Penelitian ini bertujuan untuk menyediakan model peramalan yang memprediksi kasus COVID-19 yang dikonfirmasi di kota Semarang. Penelitian ini menerapkan algoritma pembelajaran mesin yakni Artificial Neural Network (ANN) untuk memprediksi kasus COVID-19 di kota Semarang. Proses fine-tuning masing-masing model dijelaskan dalam penelitian ini dan perbandingan numerik antara ketiga model disimpulkan menggunakan ukuran evaluasi yang berbeda; mean sequence error (MSE).

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References

Azarafza, M., Azarafza, M., & Tanha, J. (2020). COVID-19 Infection Forecasting based on Deep Learning in Iran, 1–7. https://doi.org/10.1101/2020.05.16.2010418

Barman, A. (2020). Time Series Analysis and Forecasting of COVID-19 Cases Using LSTM and ARIMA Models. arXiv.

Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S., & Ciccozzi, M. (2020). Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in Brief, 29, 105340. https://doi.org/10.1016/j.dib.2020.105340

Chimmula, V. K. R., & Zhang, L. (2020). Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons, and Fractals, 135, 109864. https://doi.org/10.1016/j.chaos.2020.109864

Dong, E., Du, H., & Gardner, L. (2020, Mei). An interactive web-based dashboard to track COVID-19 in real time. The Lancet. Infectious Diseases. https://doi.org/10.1016/S1473-3099(20)30120-1

Gers, F. A., Schmidhuber, J. A., & Cummins, F. A. (2000). Learning to Forget: Continual Prediction with LSTM. Neural Comput., 12(10), 2451–2471. https://doi.org/10.1162/08997660030001501

Huang, C. J., & Kuo, P. H. (2019). Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting. IEEE Access, 7, 74822–74834. https://doi.org/10.1109/ACCESS.2019.2921238

Längkvist, M., Karlsson, L., & Loutfi, A. (2014). A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognit. Lett., 42, 11–24.

Mahalakshmi, G., Sridevi, S., & Rajaram, S. (2016). A survey on forecasting of time series data. 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE’16), 1–8.

Qin, M., Li, Z., & Du, Z. (2017). Red Tide Time Series Forecasting by Combining ARIMA and Deep Belief Network. Know.-Based Syst., 125(C), 39–52. https://doi.org/10.1016/j.knosys.2017.03.027

Tomar, A., & Gupta, N. (2020). Prediction for the spread of COVID-19 in India and effectiveness of preventive measures. The Science of the Total Environment, 728, 138762. https://doi.org/10.1016/j.scitotenv.2020.138762

Wu, Y., & Tan, H. (2016). Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework. CoRR, abs/1612.01022. Diambil dari http://arxiv.org/abs/1612.01022

Yang, Z., Zeng, Z., Wang, K., Wong, S.-S., Liang, W., Zanin, M., … He, J. (2020). Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. Journal of Thoracic Disease, 12(3), 165–174. https://doi.org/10.21037/jtd.2020.02.64

Zhao, Z., Chen, W., Wu, X., Chen, P., & Liu, J. (2017). LSTM network: a deep learning approach for short-term traffic forecast. Iet Intelligent Transport Systems, 11, 68–75.

DOI: https://doi.org/10.35475/riptek.v15i1.113

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