Volume 1, Number 2 (2016)
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Home > Journals > SCIREA Journal of Mathematics > Archive > Paper Information

Modelling Singapore Tourist Arrivals to Malaysia by Using SVM and ANN

Volume 1, Issue 2, December 2016    |    PP. 210-216    |PDF (340 K)|    Pub. Date: January 11, 2017
137 Downloads     1441 Views  

Author(s)
Rafidah Ali, University Kuala Lumpur Malaysia Institute of Industrial Technology, Persiaran Sinaran Ilmu, Bandar Seri Alam,81750 Johor Bharu Johor Malaysia
Ani Shabri, Departments of Mathematics, Science Faculty, University of Technology Malaysia, Skudai, Johor Malaysia

Abstract
The tourism industry is an increasingly important national industry for Malaysia. Government policymakers and business managers pay close attention to the development of the tourism industry. In this study, two machine learning methods, artificial neural network (ANN) and support vector machine (SVM) were utilized to predict the Singapore tourist arrival to Malaysia. This country is neighbouring country for Malaysia and tourists are more flexible to visit Malaysia using rails and road transportations. This paper aims at finding an accurate forecasting model in order to make the tourism industry grow stably. This study uses monthly time series data from 2010 (January) until 2014 (December). The experiment shows that ANN model outperform SVM base on the criteria Root Mean Absolute Error (RMSE).

Keywords
Support Vector Machine (SVM), Artificial Intelligence (AI), Artificial Neural Network (ANN)

Cite this paper
Rafidah Ali, Ani Shabri, Modelling Singapore Tourist Arrivals to Malaysia by Using SVM and ANN, SCIREA Journal of Mathematics. Vol. 1 , No. 2 , 2016 , pp. 210 - 216 .

References

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