Proyeksi Produksi Padi Kabupaten Pinrang Dengan Metode Singular Spectrum Analysis
Abstract
The increasing population growth in Pinrang Regency every year had an impact on increasing the need for food, especially rice production which was generally a staple food source in Pinrang Regency. so it was necessary to do a forecast to anticipate future food shortages. This study aimed to determine the yield of rice production in Pinrang Regency in 2021 and the level of accuracy of the method used. The results of the study obtained forecast for rice production in Pinrang Regency in 2021 using the Singular Spectrum Analysis (SSA) method, respectively from the first quarter to the third quarter of 33603 tons, 25988 tons, and 43234 tons with the level of forecasting accuracy based on standard MAPE value obtained by 4.97 %. The MAPE value obtained was less than 10 % and close to 0 %, meaning that the SSA method with windows length 9 and 7 groups were very accurate to be used to predict rice production in Pinrang Regency.
References
Asrof, Asnafiyah dkk. (2017). Peramalan Produksi Cabai Merah di Jawa Barat Menggunakan Metode Singular Spectrum Analysis (SSA).(Jurnal Statistika Vol.17 No.2)
Jatmiko, yogo Aryo, dkk (2017), Perbandingan Keakuratan Hasil Peramalan Produksi Bawang Merah Metode Holt Winter dengan Singular Spectrum Analysis. (Jurnal Matematika “MANTIK” Vol 03, No.01)
Hardi, Desy Tresnowati. (2019). Peramalan Produk Domestik Bruto (PDB) Sektor Pertanian, Kehutanan, dan Perikanan Menggunakan Singular Spectrum Analysis (SSA). (Jurnal Gaussian, Volume 8, Nomor 1)
Tarigan,Senang ukurta. (2018).Peramalan Data Menggunakan Singular Spectrum Analysis dengan Metode Peramalan Linear Recurrent Formula. (Yogyakarta : Program Studi Satistika UGM)
Sitohang, Yoseph O., & Darmawan, Gumgum.(2017). Perbandingan Akurasi Recurrent Forecasting dan Vector Forecasting pada Metode Peramalan Jumlah Wisatawan Mancanegara yang Masuk Melalui bandara Ngurah Rai Bali Tahun 2017. (Jurnal Euclid Vol. 5 No. 1)
Darmawan, gumgum (2016). Identifikasi Pola Data Curah Hujan pada Proses Grouping dalam Metode Singular Spectrum Analysis. (Seminar Nasional Pendidikan Matematika)
Briceno, Hector., dkk. (2013). Singular Spectrum Analysisfor Forecasting of Electric Load Demand. (Jurnal Chemical Engineering Transaction, Vol.33)
Kalantari, Mahdi. (2021). Forecasting COVID-19 Pandemic Using Optimal Singular Spectrum Analysis. (Jurnal Chaos, Solition and Fractals : Nonlinear Science, and Nonequilibrium and Complex Phenomena)
Leon, Steven J. (1998). Linear Algebra with Aplication Fifith Edition. (Pretincehall, United States)
Aswi dan Sukarna, (2006). Analisis Deret Waktu. (Makassar:Andira Publisher).
George E.P, Box dkk. (2008). Time Series Analysis Forecasting and Control. (Canada: John Wiley &Sons)
Sainah, Awit Marwati. (2018). Akurasi Peranalan Long Horizon dengan Singular Spectrum Analysis (eJurnal of Sunan gunung Jati StateIslmaic University (UIN) Vol. 3 No.2)
Khaeri, Hirlan, dkk. (2017). Penerapan Metode Singular Spectrum Analysis (SSA) pada Peramalan Jumlah Kereta Api di Indonesia Tahun 2017. (jurnal Euclid, Vol. 5 No. 1)
Ariyanti, Gregoria. (2010). Dekomposisi Nilai Singular dan Aplikasinya. (Yogyakarta : Seminar Nasional Matematika dan Pendidikan Matematika)
Golyandina dan Zhigljavsky. (2013). Singular Spectrum Analysis for Time Series. STAT ME.
Andika, Gilang Bimasakti (2010). Peramalan Nilai Tukar Petani menggunakan meode Singular Spectrum Analysis, (Denpasar : Program Studi Matematika Udayana)
Koepke, H.A., & Clarke, B.S. (2010).On the limits of Clustering in High Dimension Via Cost Function. (Jurnal Staistical and Data Mining,4)
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