Aplikasi Metode Singular Spectral Analysis (SSA) dalam Peramalan Pertumbuhan Ekonomi Indonesia Tahun 2017
DOI:
https://doi.org/10.15642/mantik.2017.3.1.5-12Keywords:
Gross domestic product; Singular spectral analysis method; ForecastingAbstract
The purpose of this study was to look at seasonal patterns in the data of Gross Domestic Product (GDP) quarterly in the year 2000-2016 and the implementation of Singular Spectral Analysis (SSA) in the data of GDP to predict the data of GDP in 2017. The SSA method used is the method of recurrent forecasting with bootstrap confidence interval to look at its beliefs of the interval. The source of data derived from the data of GDP in 2000-2016 with the base year in 2000 compiled by the Central Statistics Agency (CSA). The results showed that the SSA method can be used as a reliable method and can be valid that view from the value of MAPE size is 0.82 and the size of the tracking signal at -4.00.
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References
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[2] Badan Pusat Statistik. (2010). Seasonal Adjustment dan Peramalan PDB Triwulanan. Jakarta: Badan Pusat Statistik.
[3] Darmawan, G. (2016). Identifikasi Pola Data Curah Hujan pada Proses Grouping dalam Metode Singular Spectrum Analysis. Prosiding Sempoa: Seminar Nasional, Pameran Alat Peraga, dan Olimpiade Matematika (pp. 415-424). Surakarta: Universitas Muhammadiyah Surakarta.
[4] Darmawan, G., Hendrawati, T., & Arisanti, R. (2015). Model Auto Singular Spectrum Untuk Meramalkan Kejadian Banjir di Bandung dan Sekitarnya. Prosiding Seminar Nasional Matematika dan Pendidikan Matematika UNY 2015 (pp. 457-462). Yogyakarta: Universitas Negeri Yogyakarta.
[5] Efron, B., & Thibshirani, R. (1986). Bootstrap Method for Standar Errors, Confidence Intervals, and Other Measures of Statistical Accuracy. Statistical Science, 1(1), 54-75.
[6] Golyandina, N., & Korobeynikov, A. (2012). Basic Singular Spectrum Analysis and Forecasting with R. Rusia: Faculty of Mathematics and Mechanic, St. Petersburgh State University.
[7] Golyandina, N., & Zhigljavsky, A.A. (2013). Singular Spectrum Analysis for Time Series. New York: Springer.
[8] Golyandina, N., Nekrutkin, V., & Zhigljavsky, A.A. (2001). Analysis of Time Series Structure: SSA and Related Techniques. New York: Chapman & Hall/CRC.
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[10] Leeuw, J.D., & Crutcher, P. (2009). Singular Spectrum Analysis in R. Los Angeles: Department of Statistics, University of California.
[11] Sungkawa, I., & Megasari, R.T. (2011). Penerapan Ukuran Ketepatan Nilai Ramalan Data Deret Waktu dalam Selesi Model Peramalan Volume Penjualan PT Satria Mandiri Citra Mulia. Jakarta: Departemen Matematika dan Statistik, Universitas Binus.
[12] Abraham, B., & Ledolter, J. (1983). Statistical Methods for Forecasting. New York: John Wiley.
[13] Darmawan, G., Handoko, B., & Suparman, Y. (2016). Seasonal Test for Non-Stationary Time Series Data by Means of Periodogram Analysis. Prosiding the 2nd International Conference on Applied Statistics (pp. 74-81). Bandung: Fakultas Matematika dan Ilmu Pengetahuan, Universitas Padjajaran.
[14] Prahmana, R.C.I. (2017). Design Research (Teori dan Implementasinya: Suatu Pengantar). Jakarta: Rajawali Pers.
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Published
2017-10-26
How to Cite
Siregar, R. S. K., Prariesa, D., & Darmawan, G. (2017). Aplikasi Metode Singular Spectral Analysis (SSA) dalam Peramalan Pertumbuhan Ekonomi Indonesia Tahun 2017. Jurnal Matematika MANTIK, 3(1), 5–12. https://doi.org/10.15642/mantik.2017.3.1.5-12
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