• Ahmad Ashril Rizal
  • Abdurrahim Abdurrahim
  • Andi Sofyan Anas
Keywords: Prediction, Time Series, Nonlinear, Autoregressive, Moving Average, Narma


Prediction is one of the elements for decision support in the future. Because of the unavailability of natural resources such as oil and gas, forest products or large-scale manufacturing industries in Lombok, tourism has become a leading sector in economics development. Prediction of tourist arrivals needs to be done to support policies related to tourism development. There are two basic methods of prediction: arima and neural network. Arima is good for prediction with stationary dataset, while neural networks are either used for prediction with stationary or non-stationary data. Previous research related to the prediction of tourist arrivals using Recurrent Neural Network approach with Extended Kalman Filter. This research tries to predict time series data from tourist arrivals by Nonlinear Autoregressive Moving Average (NARMA) approach. Predicted results based on Mean Square Error (MSE), the best prediction result is given on ARMA model (5,1,0) with MSE 0.178.


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