A comparative forecasting model of COVID-19 case in Indonesia

T. W. Septiarini, M. R. Taufik, T. A.E. Prasetya

Research output: Contribution to journalConference articlepeer-review


COVID-19 had been a disaster in Indonesia. Moreover, it is needed a study to analyze the trend of this case. The objectives of this study were (1) to propose the model for predicting COVID-19 using exponential smoothing, autoregressive integrated moving average (ARIMA), neural network, and fuzzy time series and (2) to compare the performance for each model by using RMSE as evaluation tool. In this study, the splitting data is implemented by 3:1 ratio on train and test data set. The results show that the neural network has the smallest error, 772.46 for RMSE. It means that neural network perform better than other forecasting model. Once, the characteristic data had big impact to building forecasting model whether in classical or modern model.

Original languageEnglish
Article number042020
JournalJournal of Physics: Conference Series
Issue number4
Publication statusPublished - 14 Jun 2021
Event7th International Conference on Mathematics, Science, and Education 2020, ICMSE 2020 - Semarang, Virtual, Indonesia
Duration: 6 Oct 20206 Oct 2020


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