Comparison of Fuzzy Time Series Lee, Chen, and Singh on Forecasting Foreign Tourist Arrivals to Indonesia in 2023 (Jurnal Matematika, Statistika dan Komputasi, 2024)
Tourism in Indonesia is one of the most reliable sectors because it can increase economic growth. Foreign tourist visits to Indonesia fluctuate every month, so forecasting needs to be done in order to help the Indonesian government in making decisions regarding the development process of the tourism industry to be right on target, efficient, and effective. The purpose of this research is to compare the Lee, Chen, and Singh fuzzy time series methods in forecasting foreign tourist visits to Indonesia.The data used in this study are monthly data on the number of foreign tourist visits to Indonesia from July 2014 to December 2023. The methods used for forecasting are Lee's fuzzy time series method, Chen's fuzzy time series, and Singh's fuzzy time series. The results of this study obtained MAPE values for in-sample data of foreign tourist visits to Indonesia using the Lee, Chen, and Singh fuzzy time series methods are 9.81%, 10.35%, and 2.77%, respectively. The MAPE values for out-sample data of foreign tourist arrivals to Indonesia using the Lee, Chen, and Singh fuzzy time series methods are 12.99%, 13.35%, 0.80%, respectively. From the MAPE value of in-sample data and out-sample data, it can be concluded that Singh's fuzzy time series has the smallest error value, so Singh's fuzzy time series is better and more accurate in forecasting foreign tourist visits to Indonesia.
Keywords: oreign tourist arrivals; fuzzy time series; Chen fuzzy; Lee fuzzy; Singh fuzzy