AbstrakTujuan dari penelitian ini adalah untuk melihat pola musiman pada data Produk Domestik Bruto (PDB) riil Triwulanan Tahun 2000-2016 dan melihat penerapan dari Singular Spectral Analysis (SSA) pada data PDB tersebut untuk meramal data PDB pada tahun 2017. Adapun metode SSA yang digunakan adalah metode recurrent forecasting dengan bootstrap confidence interval untuk melihat selang kepercayaannya. Sumber data berasal dari data PDB tahun 2000-2016 tahun dasar 2000 yang dikumpulkan oleh Badan Pusat Statistik (BPS). Hasilnya menunjukkan bahwa metode SSA dapat dijadikan metode yang handal dan dapat dikatakan valid dilihat dari nilai ukuran MAPE 0.82 dan ukuran tracking signal sebesar -4.00. AbstractThe 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 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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