Hizir Sofyan, Nidya Tatsara, Yolanda, Tarmizi Usman, Irvanizam
Forecasting is a common thing to capture events in future based on previous information. However, some classical time-series methods, including moving average (MA), autoregressive integrated moving average (ARIMA), sea-sonal autoregressive integrated moving average (SARIMA), and simple expo-nential smoothing (SES), have limitations in predicting nonlinear time-series data. Therefore, this paper aims to utilize the adaptive neuro-fuzzy inference system (ANFIS) model, a combination of the fuzzy inference system (FIS) and neural network architecture to forecast a nonlinear rainfall problem. This model can capture the non-linear data, adaptation capability, and speedy learning ca-pacity. We used the data consisting of temperature (ºC), humidity (%), and wind speed (km/hour) as input variables and rainfall (millimeter) as an output vari-able at two stations and one rain post in Aceh Besar District, from January 2009 to December 2019. The results demonstrated that ANFIS with generalized Bell (gBell) membership function on epoch 10 can successfully conduct rainfall forecasting in Aceh Besar District with the best-predicted value. The mean absolute percentage error (MAPE) of the prediction at the Meteorology, Climatology, and Geophysics Agency (MCGA) Station or Badan Meteorologi, Klimatologi dan Geofisika (BMKG) Indrapuri is 6.73% for 80% of the training dataset and 20% of the testing dataset. © 2025, Institute of Advanced Engineering and Science. All rights reserved.
Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia; Graduate School of Mathematics and Applied Sciences, School of Postgraduate, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia
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