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Fariz Fadillah Mardianto, M., Fajar Cahyono, E., Syarifah, L., & Andriani, P. (2019). Prediction of the Number of Foreign Tourist Arrival in Indonesia Halal Tourism Entrance using Simultaneously Fourier Series Estimator. KnE Social Sciences, 3(13), 1093–1104. https://doi.org/10.18502/kss.v3i13.4270

https://doi.org/10.18502/kss.v3i13.4270

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authors

  • M Fariz Fadillah Mardianto
    No author data available.
  • E Fajar Cahyono
    No author data available.
  • L Syarifah
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  • P Andriani
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Published Date

  • Mar 31, 2019

Prediction of the Number of Foreign Tourist Arrival in Indonesia Halal Tourism Entrance using Simultaneously Fourier Series Estimator

KnE Social Sciences / The 2nd International Conference on Islamic Economics, Business, and Philanthropy (2nd ICIEBP) / Pages 1093–1104

Abstract

Indonesia is the largest Muslim country in terms of population and area. Thus, it is natural that the halal tourism develops in Indonesia. In 2015, the Indonesian Ministry of Tourism established three provinces in Indonesia as halal tourism destinations. The three provinces are Nangroe Aceh Darussalam, West Sumatra, and West Nusa Tenggara. Previously, Central Java, East Java, West Java and Banten provinces have developed Islamic tourism areas. Islamic tourism in Indonesia contributes to increasing national foreign exchange reserves, and empowering communities around tourism objects. Therefore, prediction the number of foreign tourist arrival in Indonesia halal tourim entrance is important to make a plan for determine next regulation and target satisfied. The entrance that be used is Kualanamu, Minangkabau, Soekarno Hatta, Adi Sucipto, Juanda, and Lombok Praya International Airport. Prediction that be done simultaneously because Statistically there are correlations from the data in each entrance. The research method used is nonparametric regression based on Fourier series estimator simultaneously, because the data does not form a certain pattern and it is periodic. The results obtained from this study indicate that estimator has satisfied the goodness of model, so it can be used to make prediction.

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