Peramalan Luas Panen Padi Indonesia Dengan Model ETS (Error, Trend, Seasonal)
Abstract
Makalah ini bertujuan untuk mengevaluasi akurasi perkiraan potensi luas panen padi yang dihasilkan melalui metode Kerangka Sampel Area (KSA) dan menyajikan alternatif metode perkiraan luas panen padi dengan mempertimbangkan unsur musimam pada data. Data yang digunakan dalam penelitian ini adalah data realisasi dan potensi luas panen padi hasil Survei KSA periode Januari 2018 sampai dengan Desember 2020. Evaluasi akurasi yang digunakan yaitu nilai Mean Absolute Error (MAE), Root Mean Square Error (RMSE), dan Mean Absolute Percentage Error (MAPE). Metode alternatif peramalan yang digunakan adalah metode ETS. Hasil penelitian menunjukkan perkiraan luas panen padi satu bulan ke depan dengan menggunakan metode KSA memiliki tingkat akurasi yang relatif tinggi, ditunjukkan oleh nilai MAPE yang relatif kecil yakni sebesar 7,52 persen. Sementara itu, ramalan panen menggunakan metode ETS memiliki nilai MAPE, MAE, dan RMSE yang relatif lebih tinggi dibandingkan dengan prediksi panen berdasarkan hasil amatan KSA. Namun, kesalahan peramalan terhadap realisasi luas panen cenderung semakin mengecil seiring dengan penambahan data training. Dengan kata lain, semakin banyak titik data untuk peramalan, semakin baik akurasi hasil peramalan yang dihasilkan oleh metode ETS. Oleh karena itu, model ini dapat dipertimbangkan dalam peramalan luas panen padi dengan menggunakan data hasil Survei KSA ke depannya.
References
Peter Rosner, L., & McCulloch, N. 2008. A note on rice production, consumption and import data in Indonesia. Bulletin of Indonesian Economic Studies, 44(1), 81-92.
Ruslan, K. 2019. Improving Indonesia’s Food Statistics through the Area Sampling Frame Method. Center for Indonesian Policy Studies. doi: 10.35497/287781
Hyndman, R. J., A. B. Koehler, R. D. Snyder and S. Grose. 2002. A state space framework for automatic forecasting using exponential smoothing methods, International Journal of Forecasting, 18(3), 439–454.
Mubekti & Sumargana, L. 2016. Pendekatan Kerangka Sampel Area untuk Estimasi dan Peramalan Produksi Padi. Jurnal PANGAN, 25 (2), 71 – 82.
Wigton, W.H., & Bormann, P. 1978. Guide to area sampling frame construction utilizing satellite imagery. Second International Training Course in Remote Sensing Applications for Agriculture: Crop Statistics and Agricultural Census, 25 April-13 May 1977, Rome.
Muhlis. 2018. Area Sampling Frame: A New Approach to Reform Agricultural Data Collection. Asia-Pacific Economic Statistics Week. 2018. Tersedia dari : https://communities.unescap.org/asia-pacific-economic-statistics/apes-2018-featured-papers.
Pan Y, Wang M, Wei G, Wei F, Shi K, Li L, Sun G. 2010. Application of Area-frame sampling for agricultural statistics in China. In Proceedings of the Fifth International Conference on Agricultural Statistics (ICAS-V). Rome: FAO. Teresedia dari: fao.org.
Durante AC, Lapitan P, Megill D, Rao LN. 2018. Improving Paddy Rice Statistics Using Area Sampling Frame Technique. Asian Development Bank Economics Working Paper Series. 2018 Nov 28: 565. doi: 10.22617/WPS189643-2.
Prasetyo, Octavia Rizky, Kadir, and Amalia, Ratna Rizki. 2020. A Pilot Project of Area Sampling Frame for Maize Statistics: Indonesia’s Experience, 36(4): 997 – 1006.
Gallego, FJ. 2015. Area Sampling Frames for Agricultural and Environmental Statistics. EUR 27595 EN. doi: 10.2788/88253.
Gallego, F.J. 1995. Sampling Frames of Square Segments. ReportEUR1631EN.1. Joint Research Centre, European Commission, Luxembourg
Badan Pusat Statistik (BPS). 2018. Pedoman Teknis Pendataan Statistik Pertanian Tanaman Pangan Terintegrasi dengan Metode Kerangka Sampel Area (KSA) 2018. Jakarta: Badan Pusat Statistik.
Hyndman & Athanasopoulos. 2018. Forecasting: Principles and Practices (2nd Edition). OTexts.org/fpp2/
Hyndman, R. J., & Koehler, A. B. 2006. Another look at measures of forecast accuracy. International journal of forecasting, 22 (4), 679-688.
Ord, J. K., Koehler, A. B. & Snyder, R. D. 1997. ‘Estimation and prediction for a class of dynamic nonlinear statistical models’, Journal of the American Statistical Association 92, 1621– 1629.
Jofipasi et al. 2017. Selection for the best ETS (error, trend, seasonal) model to forecast weather in the Aceh Besar District. IOP Conf. Series: Materials Science and Engineering 352 (2018) 012055 doi:10.1088/1757-899X/352/1/012055
Shamshad et al. 2019. Modeling and Forecasting Weather Parameters using ANN-MLP, ARIMA and ETS model: A case study for Lahore, Pakistan. International Journal of Scientific & Engineering Research. 10(4): 351 ISSN 2229-5518
Ke, G., Hu, Y., Huang, X., Peng, X., Lei, M., Huang, C., Gu, L., Xian, P., & Yang, D. 2016. Epidemiological analysis of hemorrhagic fever with renal syndrome in China with the seasonal-trend decomposition method and the exponential smoothing model. Scientific reports, 6, 39350. https://doi.org/10.1038/srep39350
Zhanao Sun. 2020. Comparison of Trend Forecast Using ARIMA and ETS Models for S&P500 Close Price. ICEBI 2020: 2020 The 4th International Conference on E-Business and Internet October 2020 Pages 57–60 https://doi.org/10.1145/3436209.3436894
Hyndman, R. J., & Khandakar, Y. 2008. Automatic time series forecasting: the forecast package for R. Journal of Statistical Software, 27 (3). http://www.jstatsoft.org/v27/i03
Er. Garima Jain, Bhawna Mallick,A Study of Time Series Models ARIMA and ETS", International Journal of Modern Education and Computer Science (IJMECS). 9(4): pp.57-63, 2017. DOI: 10.5815/ijmecs.2017.04.07
Bowerman, B.L. and O'Connell, R.T. (1993, 2004). Forecasting and Time Series: An Applied Approach,. 3 and 4 edition, Duxbury Press: USA.
Lewis, C. D. 1982. Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. Butterworth-Heinemann.
Zivot, E., & Wang, J. 2006. Time Series Concepts. Modeling Financial Time Series with S-PLUS®, 57-110.
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