Open AccessPERBANDINGAN METODE SEASONAL ARIMA DAN EXTREME LEARNING MACHINE PADA PERAMALAN JUMLAH WISATAWAN MANCANEGARA KE BALI
Author(s) -
Bayu Galih Prianda,
Edy Widodo
Publication year - 2021
Publication title -
barekeng
Language(s) - English
Resource type - Journals
eISSN - 2615-3017
pISSN - 1978-7227
DOI - 10.30598/barekengvol15iss4pp639-650
Subject(s) - autoregressive integrated moving average , extreme learning machine , seasonality , tourism , artificial neural network , statistics , computer science , geography , time series , artificial intelligence , mathematics , archaeology
Bali Island of the Gods is one of the wealth of very popular tourist destinations and has the highest number of foreign tourists in Indonesia. It is very necessary to do more in-depth learning related to the projections or forecasting of foreign tourist visits to Bali at a certain period of time. Forecasting analysis used is to compare two methods, namely the Seasonal ARIMA method (SARIMA) and Extreme Learning Machine (ELM). The SARIMA method is a statistical method commonly used in forecasting time series data that contains seasonality and has good accuracy. While the ELM method is a new learning method of artificial neural networks that has fast learning speed and good accuracy. The results obtained indicate that the Seasonal ARIMA method is a better method used to predict the number of tourists to Bali in this case, because it has a smaller forecasting MAPE value of 4.97%. While the ELM method has a forecasting MAPE value of 7.62%.