KAJIAN MODEL PERAMALAN KUNJUNGAN WISATAWAN MANCANEGARA DI BANDARA KUALANAMU MEDAN TANPA DAN DENGAN KOVARIAT

Authors

  • Isti Rochayati Badan Pusat Statistik, Indonesia
  • Utami Dyah Syafitri Dept. of Statistics, IPB University
  • I Made Sumertajaya Dept. of Statistics, IPB University
  • Indonesian Journal of Statistics and Its Applications IJSA

DOI:

https://doi.org/10.29244/ijsa.v3i1.171

Keywords:

sarima, sarimax, tourist, varima, varimax

Abstract

Foreign tourist arrivals could be considered as time series data. Modelling these data could make use of internal and external factors. The techniques employed here to model these time series data are SARIMA, SARIMAX, VARIMA, and VARIMAX. SARIMA is a model for seasonal data and VARIMA is a model for multivariate time series data. If some explanatory variables are incorporated and have significant influence on the response, the former two models become SARIMAX and VARIMAX respectively. Three stages of creating the model are model identification, parameter estimation, and model diagnostics. The variables used in this study were foreign tourist visits, international passenger arrivals, inflation rates, currency exchange rates, and Gross Regional Domestic Product (GRDP) over the period of 2010-2017. All four models fulfill their model assumptions and therefore could be applied. The best model of foreign tourist arrivals was VARIMA with the value of MAPE testing data = 6.123.

Downloads

Download data is not yet available.

References

Anggara, P.F. (2013). Analisis Dampak Kedatangan Wisatawan Asing dan Faktor Penentunya terhadap Pertumbuhan Ekonomi Indonesia 1974-2011. Jakarta: Fakultas Ekonomi Universitas Indonesia.

Anggraeni, W., Vinarti, R. A., & Kurniawati, Y. D. (2015). Performance comparisons between arima and arimax method in moslem kids clothes demand forecasting: Case study. Procedia Computer Science, 72, 630-637.

[BPS] Badan Pusat Statistik. (2016a). Statistik Angkutan Udara. Jakarta: Badan Pusat Statistik.

[BPS] Badan Pusat Statistik. (2016b). Statistik Kunjungan Wisatawan Mancanegara. Jakarta: Badan Pusat Statistik.

[BPS] Badan Pusat Statistik. (2017). Indeks Harga Konsumen di 82 Kota di Indonesia (2012=100) 2017. Jakarta: Badan Pusat Statistik.

Cryer, J. D. (1986). Time Series Analysis. Wadsworth Publ. Co.

Gujarati, D. (2003). Basic Econometrics. Forth Edition. Singapura: McGraw-Hill.

Maharani, A. A., & Darmawan, A. (2018). Pengaruh Inflasi, Nilai Tukar dan Pertumbuhan Ekonomi Singapura terhadap Kunjungan Wisatawan Singapura di Indonesia. Jurnal Administrasi Bisnis, 56(1).

Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting. John Wiley & Sons.

Ulyah, S. M., Susilaningrum, D., & Suhartono, S. (2014). Peramalan Volume Penjualan Total Sepeda Motor di Kabupaten Bojonegoro dan Lamongan dengan Pendekatan Model ARIMAX dan VARX. Jurnal Sains dan Seni ITS, 3(2), D230-D235.

Sutthichaimethee, P. (2017). VARIMAX Model to Forecast the emission of Carbon Dioxide from Energy Consumption in Rubber and Petroleum industries sectors in Thailand. Journal of Ecological Engineering, 18(3), 112-117.

Published

2019-02-28

How to Cite

Rochayati, I., Syafitri, U. D., Sumertajaya, I. M., & IJSA, I. J. of S. and I. A. (2019). KAJIAN MODEL PERAMALAN KUNJUNGAN WISATAWAN MANCANEGARA DI BANDARA KUALANAMU MEDAN TANPA DAN DENGAN KOVARIAT. Indonesian Journal of Statistics and Its Applications, 3(1), 18–32. https://doi.org/10.29244/ijsa.v3i1.171

Issue

Section

Articles

Most read articles by the same author(s)

1 2 3 > >>