FOREIGN TOURIST ARRIVAL FORECASTING TO BALI USING CASCADE FORWARD BACKPROPAGATION
Bali has a recognized tourism potential in the world arena. In order to improve the quality and development of the tourism sector in the midst of global competition, it is necessary to formulate appropriate strategies by decision makers such as private parties and government. In support of more accurate decision making, the authors make a system of forecasting the number of foreign tourist visits to Bali Province using Cascade Forward Backpropagation (CFB) method with coverage of Australia, Japan, and United Kingdom which are the top 3 countries with the highest foreign tourist arrival to Bali in that years. Factors used as input in forecasting include the number of visits of foreign tourists the previous year, the population of countries of origin of foreign tourists, Gross Domestic Product at current prices of countries of origin of foreign tourists, and Relative Consumer Price Index Origin of foreign tourists. In this study, optimization of activation function parameters, hidden neurons, and learning rate to obtain forecasting results with the lowest error rate. Forecasting results using the CFB method produces a fairly good accuracy with MAPE range of 6 - 30% where the activation function tanh work better than sigmoid activation function.
 Contributor, “Readers’ Choice Awards 2017,” DestinAsian Media Group, Singapore, 2017.
 Disparda.baliprov.go.id, “Number of Foreign Tourists Arrival to the Province of Bali based on Nationality,” Disparda.baliprov.go.id, 2017. .
 Asean.org, “Asean Economy Community,” Asean.org, 2017. http://asean.org/asean-economic-community/ (accessed Nov. 20, 2017).
 G. P. Zhang, Neural Networks in Business Forecasting. 2004.
 L. C. David, Industrial and Business Forecasting Method. London: Butterworths, 1982.
 M. Hertinmalyana, A. H. Rahayu, and R. R. Wati, “Analysis of Demand and Consumption of International Visitors to Indonesia (from selected countries),” 13th Glob. Forum Tour. Stat., p. 400, 2014.
 Data.worldbank.org, “World Bank Open Data,” data.worldbank.org. .
 W. O. Vihikan, I. K. G. D. Putra, and I. P. A. Dharmaadi, “Foreign tourist arrivals forecasting using Recurrent Neural Network Backpropagation Through Time,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 15, no. 3, pp. 1257–1264, 2017, doi: 10.12928/TELKOMNIKA.v15i3.5993.
 D. S. Badde, A. Gupta, and V. K. Patki, “Cascade and Feed Forward Back propagation Artificial Neural Network Models for Prediction of Compressive Strength of Ready Mix Concrete,” IOSR J. Mech. Civ. Eng., no. 2278–1684, pp. 1–6, 2009.
 S. Tengeleng and N. Armand, “Performance of using cascade forward back propagation neural networks for estimating rain parameters with rain drop size distribution,” Atmosphere (Basel)., vol. 5, no. 2, pp. 454–472, 2014, doi: 10.3390/atmos5020454.
 B. P. Santosa and Ashari, Statistical Analysis with Microsoft Excel and SPSS. Yogyakarta: Andi Offset, 2005.
 Badan Pusat Statistik, “Number of Foreign Tourist Arrivals based on Entrance and Nationality,” bps.go.id, 2017.
 Badan Pusat Statistik Provinsi Bali, “Statistics of Foreign Tourists in Bali Province,” bps.go.id, 2016.