The Forecasting Technique Using SSA-SVM Applied to Foreign Tourist Arrivals to Bali
Yosep Oktavianus Sitohang, Yudhie Andriyana, Anna Chadidjah
In order to achieve a targeted number of foreign tourist arrivals set by the Indonesian government in 2017, we need to predict the number of foreign tourist arrivals. As a major tourist destination in Indonesia, Bali plays an important role in determining the target. According to the characteristic of the tourist arrivals data, one shows that we need a more flexible forecasting technique. In this case we propose to use a Support Vector Machine (SVM) technique. Furthermore, the effects of noise components have to be filtered. Singular Spectrum Analysis (SSA) plays an important role in filtering such noise. Therefore, the combination of these two methods (SSA-SVM) will be used to predict the number of foreign tourist arrivals to Bali in 2017. The performance of SSA-SVM is evaluated via simulation studies and applied to tourist arrivals data in Bali. As the results, SSA-SVM shows better performances compare to other methods.
foreign tourist, singular spectrum analysis, support vector machine