Perbandingan Metode Peramalan Jumlah Produksi Palm Kernel Oil (PKO) Menggunakan Metode Double Moving Average, Double Exponential Smothing dan Box Jenkins
Keywords:Double Exponential Smothing
AbstractOne of Indonesia's significant results is oil palm. The reality of this plantation is not only owned by the government (BUMN) but also the private sector. Every period, the company does forecasting in terms of production, especially for the next period. Among them is to set production targets, company operations, and financial planning. Based on this, a study was conducted with the aim to predict the amount of palm kernel oil (PKO) production at PT. Mitra Mendawai Sejati for the next six (6) months. The method used is Double Moving Average, Double Exponential Smoothing and Box Jenkins. While the data used is historical data from the amount of palm kernel oil production for five (5) years obtained from the company. Based on the results of the study, received the forecast value of the Suayap output in 2019 with the best method, namely the Double Exponential Smoothing method. Based on the forecast we got in January at 949181.5 Kg, February at 963505.8 Kg, March at 977830.1 Kg, April at 992154.4 Kg, May at 1006478.6 Kg and June 1020802.9 Kg with MSE value of 47031163817, and RMSE of 216866.7 and parameter values (optimum weighting) for α = 0.616667 and β = 0.1548939
A. P. Sutiyono, ”Outlook Industry Perkebunan 2009,” 30 November 2009 2009. [Online]. Available: http://www.asiasecurities.co.id.
W. S. M. Makridakis S., Metode dan Aplikasi Peramalan, Jakarta: Erlangga, 1999.
P. Subagyo, Statistik Deskriptif, Yogyakarta: BPFE-Yogyakarta, 2003.
Puspitaningrum, Moving Average (ARIMA) dan Exponential Smoothing, 2015.
M. Firdaus, Analisis Deret Waktu Satu Ragam, Jakarta: IPB Press, 2006.
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