Forecasting Model Selection of Curly Red Chili Price at Retail Level
DOI:
https://doi.org/10.32734/injar.v2i1.859Keywords:
curly red chili, forecasting, retailAbstract
Chilli is one of strategic commodity in Indonesia due to its contribution to inflation level. For this reason, future price information is very importance for designing price policy. Future price merely can be provided by conducting a price forecasting. Various forecasting models can be applied for this purpose; the problem is which the best model for forecasting is. This study aims to select the most accurate forecasting model of curly red chili prices at the retail level. The data used are monthly data, from 2011 - 2017. Five forecasting models are applied and estimated including Moving Average, Single Exponential Smoothing, Double Exponential Smoothing, Decomposition, and ARIMA. The best model is selected based on the smallest MAPE, MSE and MAD values. The results show that the most accurate forecasting model is ARIMA (1,1,9).
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References
Kementerian Pertanian, “Outlook cabai merah”, Pusat Data dan Informasi Pertanian. Kementerian Pertanian. Jakarta, 2016.
M. Farid and N. A. Subekti, “Tinjauan terhadap produksi, konsumsi, distribusi dan dinamika harga cabe di Indonesia,” Buletin Ilmiah Litbang Perdagangan, vol. 6, no. 2, pp. 211-233, 2012. [Online]. Available: http://jurnal.kemendag.go.id/index.php/bilp/article/view/132.
Bank Indonesia, “Inflasi Juni 2018 Terjaga Didukung Harga Pangan yang Terkendali,” 2018. [Online] Available: https://www.bi.go.id/id/moneter/...inflasi/.../Analisis%20Inflasi%20-%20Juni18.pdf
V. Gaspersz, Production Planning and Inventory Control. Jakarta: Gramedia Pustaka Utama, 2005.
K. Sukiyono and Rosdiana, “Pendugaan model peramalan harga beras pada tingkat grosir,” AGRISEP, vol. 17, no. 1, pp. 23-30, 2018. [Online] Available: https://ejournal.unib.ac.id/index.php/agrisep/article/view/4503
Novanda, R. Rizki., E. Sumartono, P. S. Asriani, E. Yuliarti, K. Sukiyono, B. S. Priyono, Irnad, Reswita, M. Suryanty, and V. Octalia, “A comparison of various forecasting techniques for coffee prices,” Journal of Physics: Conference Series. 1114, 2018. [Online] Available: https://iopscience.iop.org/article/10.1088/1742-6596/1114/1/012119/pdf.
K. Sukiyono, M. Nabiu, B. Sumantri, R. R. Novanda, N. N. Arianti, Sriyoto, M. Z. Yuliarso, R. Badrudin, M. M. Romdhon, and H. Mustamam, “Selecting an accurate cacao price forecasting model,” Journal of Physics: Conference Series. 1114, 2018. [Online] Available: https://iopscience.iop.org/article/10.1088/1742-6596/1114/1/012116/pdf .
E. Padang, G. Tarigan, and U. Sinulingga, “Peramalan Jumlah Penumpang Kereta Api Medan-Rantau Prapat dengan Metode Pemulusan Eksponensial Holt-Winters,” Saintia Matematika, vol. 2, pp. 161-174, 2013. [Online] Available: https://media.neliti.com/media/publications/221287-peramalan-jumlah-penumpang-kereta-api-me.pdf.
W. L. Winston, Operation Research: Application and Algorithm. 4th Ed, Thomson Brooks/Cole, 2004.
Yuniastari, N. L. A. Kartika, and I. G. P. W. W. Wirawan, “Peramalan permintaan produk perak menggunakan metode simple moving average dan exponential smoothing,” Jurnal Sistem dan Informatika, vol. 9, no. 1, pp. 97-106, 2004. [Online] Available: https://jsi.stikom-bali.ac.id/index.php/jsi/article/view/41.
Siregar, A. Ruben, and E. Riksakomara, “Pembangunan aplikasi berbasis webuntuk peramalan harga saham dengan metode moving average, exponential smoothing, dan artificial neural network,” Jurnal Teknik ITS, vol. 5, no. 2, pp. 347-352, 2016. [Online] Available: http://ejurnal.its.ac.id/index.php/teknik/article/view/17070/2927
S. I. Gass and C. M. Harris, (Eds.). Encyclopedia of Operations Research and Management Science (Centennial edition), Dordrecht, The Netherlands: Kluwer, 2000.
Noeryanti, E. Oktafiani, and F. Andriyani, “Aplikasi pemulusan eksponensial dari brown dan dari holt untuk data yang memuat trend,” Prosiding Seminar Nasional Aplikasi Sains dan Teknologi (SNAST) Periode III, pp. 447-455, 2012. [Online] Available: repository.akprind.ac.id/sites/files/.../2012/noeryanti_15454.pdf
Ruiter, Michelle de, “Using exponential smoothing methods for modelling and forecasting short-term electricity demand,” Erasmus School of Economics Bachelor Econometrics and Operations Research, 2017. [Online] Available: http://hdl.handle.net/2105/38564
C. C. Holt, Forecasting Trends and Seasonals by Exponentially Weighted Averages, O. N. R. Memorandum 52/1957, Carnegie Institute of Technology, 1957.
S. Makridakis, S. C. Wheelwright, and R. J. Hyndman, Forecasting Methods and Applications. New York: Wiley, 1998. [Online] Available: www.forecastingprinciples.com/files/MakridakisbyFaria.pdf
O. Kendek, J. J. D. Prang, and M. Paendong, “Prediksi jumlah pengunjung perpustakaan universitas sam ratulangi manado menggunakan metode dekomposisi,” JdC, vol. 3, no. 1, pp. 73-80, 2014. [Online] Available: https://ejournal.unsrat.ac.id/index.php/decartesian/article/view/4000
E. Weiss, “Forecasting commodity prices using ARIMA,” Technical Analysis of Stocks & Commodities, vol. 18, no. 1, pp. 18-19, 2000.
Q. Zhang, B. D. Wang, B. He, Y. Peng, and M. L. Ren, “Singular spectrum analysis and arima hybrid model for annual runoff forecasting,” Water Resources Management, vol. 25, no. 11, pp. 2683-2703, 2011. [Online] Available:https://link.springer.com/article/10.1007/s11269-011-9833-y.
W. C. Juang, S. J. Huang, F. D. Huang, P. W. Cheng, and S. R. Wann, “Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in southern Taiwan,” BMJ Open, vol. 7, pp. 1-7, 2017. [Online] Available: https://www.ncbi.nlm.nih.gov/pubmed/29196487.
R. Anokye, E. Acheampong, I. Owusu, and E. I. Obang, “Time series analysis of malaria in Kumasi: Using ARIMA models to forecast future incidence,” Cogent social sciences, vol. 4, no. 1, pp. 1-13, 2018. [Online] Available: https://www.tandfonline.com/doi/abs/10.1080/23311886.2018.1461544.
G. E. P. Box, and G. M. Jenkins, Time Series Analysis: Forecasting and Control. Revised Edition, San Francisco: Holden-Day, 1976.
M. Ekananda, Analisis Data Time Series. Jakarta: Mitra Wacana Media, 2014.
R. Karim, A. Awala, and M. Akher, “Forecasting of wheat production in Bangladesh,” Bangladesh J. Agric. Res., vol. 35, no. 1, pp. 17–28, 2010. [Online] Available: https://www.banglajol.info/index.php/BJAR/article/view/5863/4598.
J. Jarett, Business Forecasting Methods. Cambridge, MA: Basil Blackwell, 1991.
A. H. Nasution and Prasetyawan, Perencanaan dan Pengendalian Produksi. Yogyakarta: Graha Ilmu, 2008.
R. Biri, Y. A. R. Langi, and M. S. Paedong, “Penggunaan metode smoothing eksponensial dalam meramalkan pergerakan inflasi Kota Palu,” Jurnal Ilmiah Sains, vol. 13, no. 1, pp. 68-73, 2013. [Online] Available: https://ejournal.unsrat.ac.id/index.php/JIS/article/viewFile/2035/1621.
J. G. MacKinnon, “Numerical distribution functions for unit root and cointegration tests,” Journal of Applied Econometrics, vol. 11, pp. 601–61, 1996. [Online] Available: https://doi.org/10.1002/(SICI)1099-1255(199611)11:6<601::AID-JAE417>3.0.CO;2-
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