MODELING COOKING OIL PRODUCTION WITH CRUDE PALM OIL (CPO) PRICE USING TRANSFER FUNCTION
Abstract
Indonesia is the largest palm oil producer in the world. However, Indonesia is currently experiencing a significant scarcity of cooking oil for the basic needs of the community. One of the causes of this problem is the high price of crude palm oil (CPO). The price of CPO affects the production of cooking oil produced by the cooking oil industry. CPO prices are released in the form of daily prices in the market which form a time series pattern that affects the production of cooking oil which is also produced daily in the industry. To forecast cooking oil production from CPO prices can be done with a transfer function model. The stages of analysis are forming an ARIMA model, processing input time series whitening, and output time series whitening. Checking white noise by plotting ACF αt, ACF βt. Make a CCF graph and determine the order (r,s,b). Estimate the first parameter of the disturbance series (nt) and identify the disturbance series (nt) in the ARIMA model. Next, perform a diagnostic check of the transfer function model by checking the cross-correlation between Xt & Yt and checking the autocorrelation of Xt and Yt for model feasibility testing. Then make an ACF graph of the transfer function model's residuals to test the model's suitability. The final stage is to select the best model based on the smallest AIC value, ensuring the accuracy of the selected model using MAPE value, and making predictions. As a result, the model for CPO price is an input series variable, and cooking oil production is an output series variable. However, the MAPE result was 100%, indicating that the model is not very accurate for this data. Nevertheless, by ignoring the result and continuing with the forecasting, this model shows that the forecasted values for cooking oil based on CPO price have increased from the form of the model obtained for the time data from 2013 to 2022.
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