An Application of Hybrid Forecasting Singular Spectrum Analysis – Extreme Learning Machine Method in Foreign Tourists Forecasting
International tourism is one indicator of measuring tourism development. Tourism development is important for the national economy since tourism could boost foreign exchange, create business opportunities, and provide employment opportunities. The prediction of foreign tourist numbers in the future obtained from forecasting is used as an input parameter for strategy and tourism programs planning. In this paper, the Hybrid Singular Spectrum Analysis – Extreme Learning Machine (SSA-ELM) is used to forecast the number of foreign tourists. Data used is the number of foreign tourists January 1980 - December 2017 taken from Badan Pusat Statistik (Statistics Indonesia). The result of this research concludes that Hybrid SSA-ELM performance is very good at forecasting the number of foreign tourists. It is shown by the MAPE value of 4.91 percent with eight observations out a sample.
C.H. Aladag, E. Egrioglu, and C. Kadilar, “Improvement in forecasting accuracy using the hybrid model of arfima and feed forward neural network american,” Journal of Intelligent Systems, vol.2, no.2, pp. 12-17, 2012.
D. Rahmani, “A Forecasting algorithm for singular spectrum analysis based on bootstrap linear recurrent formula coefficients,” International Journal of Energy and Statistics, vol.2, no.4, pp. 287-299, 2014.
M. Fajar, “Perbandingan kinerja peramalan pertumbuhan ekonomi Indonesia antara ARMA, FFNN dan hybrid ARMA-FFNN,” 2016. DOI:10.13140/RG.2.2.34924.36483.
M. Fajar, “Meningkatkan akurasi peramalan dengan menggunakan metode hybrid singular spectrum analysis-multilayer perceptron neural networks,” 2018. DOI: 10.13140/RG.2.2.32839.60320.
M. Fajar, “Perbandingan kinerja peramalan antara metode hybrid singular spectrum analysis-multilayer perceptrons neural network dan hybrid singular spectrum analysis-feed forward neural network pada data inflasi,” 2018. DOI: 10.13140/RG.2.2.10312.98561.
N. Golyandina, V. Nektrutkin, and A. Zhiglovsky, Analysis of Time Series: SSA and Related Techniques. Chapman and Hall/CRC, 2001.
R. Siregar, D. Prariesa, and G. Darmawan, “Aplikasi Metode Singular Spectral Analysis (SSA) dalam Peramalan Pertumbuhan Ekonomi Indonesia Tahun 2017”, mantik, vol. 3, no. 1, pp. 5-12, Oct. 2017
Y. Jatmiko, R. Rahayu, and G. Darmawan, “Perbandingan Keakuratan Hasil Peramalan Produksi Bawang Merah Metode Holt-Winters dengan Singular Spectrum Analysis (SSA)”, mantik, vol. 3, no. 1, pp. 13-22, Oct. 2017.
D. Lubis, M. Johra, and G. Darmawan, “Peramalan Indeks Harga Konsumen dengan Metode Singular Spectral Analysis (SSA) dan Seasonal Autoregressive Integrated Moving Average (SARIMA)”, mantik, vol. 3, no. 2, pp. 74-82, Oct. 2017.
Th. Alexandrv, and N. Golyandina, “automatic extraction and forecast of time series cyclic components within the framework of SSA,” . In Proceedings of the 5th St.Petersburg, 2005.
W. Makridakis, and MacGee, Metode dan Aplikasi Peramalan. Binarupa Aksara, 1999.
S. Ding, H. Zhao, Y. Zhang, X. Xu, and R. Nie, “Extreme learning machine: algorithm, theory and applications,” Artificial Intelligence Review, vol.44, no.1 pp. 103-115, 2013.
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