Pemodelan Produksi Ayam Ras di Indonesia Menggunakan Regresi dengan Sisaan Deret Waktu

  • Akhbamah Primadaniyah Febrin Departmen of Statistics, IPB
  • Itasia Dina Sulvianti Department of Statistics, IPB
  • Aji Hamim Wigena Department of Statistics, IPB
Keywords: ARIMA; broiler chicken; regression; time series errors


The production of broiler chicken has fluctuated in recent years and many factors alleged to influence the production. The purpose of this study is modeling a structural equation of forecasting the production of broiler chicken. The study use a dependent variable (Y) that is production of broiler chickens (kilo ton) and five independent variables (X) consist of broiler chicken population (million), national chicken consumption (ton/year), retail price (Rp/kg), real price of corn (Rp), and real price of Kampung chicken (Rp). The variables are time series data with errors does not spread out randomly. Modeling method used and suitable to the conditions is regression with time series errorsĀ  combined with ARIMA (Autoregressive Integrated Moving Average). The results of the regression analysis showed that only population variable and retail price variable are influencing the production of broiler chicken in Indonesia. Those two independent variables then modeled by a dependent variable using regression with time series errors. The best modeling is regression with time series errors ARIMA(1,1,0) with MAPE (Mean Average Percentage Error) value of 2.4%, RMSE (Root Mean Square Error) value of 39.800, and correlation value 0.980. The results has proved that the production of broiler chicken in Indonesia is influenced by those two variables.