Rasjid Zulfany Erlisa, Setiawan Reina, Effendi Andy
Computer Science Department, School of Computer Science, Bina Nusantara University, Jl. K.H. Syahdan No. 9, Jakarta 11480, Indonesia.
Information Systems Department, School of Information Systems, Bina Nusantara University, Jl. K.H. Syahdan No. 9, Jakarta 11480, Indonesia.
Procedia Comput Sci. 2021;179:982-988. doi: 10.1016/j.procs.2021.01.102. Epub 2021 Feb 19.
COVID-19 is a virus causing pneumonia, also known as Corona Virus Disease. The first outbreak was found in Wuhan, China, in the province of Hubei on December 2019. The objective of this paper is to predict the death and infected COVID-19 in Indonesia using Savitzky Golay Smoothing and Long Short Term Memory Neural Network model (LSTM-NN). The dataset is obtained from Humanitarian Data Exchange (HDX), containing daily information on death and infected due to COVID-19. In Indonesia, the total data collected ranges from 2 March 2020 and by 26 July 2020, with a total of 147 records. The results of these two models are compared to determine the best fitted model. The curve of LSTM-NN shows an increase in death and infected cases and the Time Series also increases, however the smoothing shows a tendency to decrease. In conclusion, LSTM-NN prediction produce better result than the Savitzky Golay Smoothing. The LSTM-NN prediction shows a distinct rise and align with the actual Time Series data.
新冠病毒病（COVID-19）是一种引发肺炎的病毒，也被称为冠状病毒病。2019年12月，中国湖北省武汉市首次发现疫情。本文旨在运用萨维茨基-戈莱平滑法和长短期记忆神经网络模型（LSTM-NN）预测印度尼西亚的新冠病毒病死亡人数和感染病例。数据集来自人道主义数据交换平台（HDX），包含新冠病毒病每日死亡和感染信息。在印度尼西亚，收集到的数据涵盖2020年3月2日至2020年7月26日，共计147条记录。对这两种模型的结果进行比较，以确定最佳拟合模型。LSTM-NN模型曲线显示死亡和感染病例呈上升趋势，时间序列也在增加，然而平滑法显示出下降趋势。总之，LSTM-NN预测结果优于萨维茨基-戈莱平滑法。LSTM-NN预测呈现出明显的上升趋势，且与实际时间序列数据相符。
