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The Forecasting of Foreign Tourists Arrival in Indonesia based on the Supply Chain Management: an Application of Artificial Neural Network and Holt winters Approaches Supriatna, Agus; Hertini, Elis; Saputra, Jumadil; Subartini, Betty; Azkiya Robbani, Alfan
International Journal of Supply Chain Management Vol 8, No 3 (2019): International Journal of Supply Chain Management (IJSCM)
Publisher : International Journal of Supply Chain Management

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Abstract

Tourism plays crucial role for improving the social and economic development of a country through open employment opportunities in surrounding tourism area. The supply chain mangement strategy can be effective in developing the turisim industry.The number of foreign tourists who come to Indonesia continues to increase from time to time. Therefore, tourist arrivals need to be forecasted to assist the government in providing optimal infrastructure and accommodation for tourists to avoid an imbalance between the number of tourists with the infrastructure and accommodation provided. The purpose of this study is to forecast the arrival of foreign tourists in Indonesia by using Artificial Neural Network and Holt-Winters approach esutilising the historical data from January 2011 to December 2017. From the calculation process, we found that MAPE (Mean Absolute Percentage Error) value of Artificial Neural Network and Holt-Winters approaches are 5.60% and 5.43%, respectively. So it can be concluded that the Holt-Winters approach is better than the Artificial Neural Network approaches in forecasting the foreign tourists arrival in Indonesia.