FOREIGN TOURIST ARRIVAL FORECASTING TO BALI USING CASCADE FORWARD BACKPROPAGATION

  • Ayu Nikki Asvikarani Computer Science Department, Faculty of Math and Science, Udayana University
  • I Made Widiartha Computer Science Department, Faculty of Math and Science, Udayana University
  • Made Agung Raharja Computer Science Department, Faculty of Math and Science, Udayana University

Abstract

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.

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References

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Published
2020-12-02
How to Cite
ASVIKARANI, Ayu Nikki; WIDIARTHA, I Made; RAHARJA, Made Agung. FOREIGN TOURIST ARRIVAL FORECASTING TO BALI USING CASCADE FORWARD BACKPROPAGATION. Jurnal Ilmiah Kursor, [S.l.], v. 10, n. 4, dec. 2020. ISSN 2301-6914. Available at: <http://kursorjournal.org/index.php/kursor/article/view/252>. Date accessed: 07 mar. 2021. doi: https://doi.org/10.21107/kursor.v10i4.252.
Section
Articles