A SARIMA APPROACH WITH PARAMETER OPTIMIZATION FOR ENHANCING FORECAST ACCURACY FOR NATIVE CHICKEN EGG PRODUCTION

Abstrak

This study aims to accurately forecast monthly native chicken egg production using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model with parameter optimization. The optimization process was conducted through a combination of auto.arima() initialization and an exhaustive grid search across the parameter space, evaluated using multiple performance metrics. The dataset comprised monthly production data from Magelang City, Indonesia, spanning the period from 2016 to 2022. The best-performing model, SARIMA (2,1,2)(1,0,1,12), achieved an R² of 0.89, MAE of 82.13, RMSE of 92.92, MAPE of 7.21%, and MASE of 0.67 on the testing set, indicating satisfactory forecasting performance. Compared with the non-optimized SARIMA baseline, the optimized model showed improved predictive accuracy. However, the residuals did not follow a normal distribution, suggesting potential limitations in model assumptions. Moreover, the study is limited by its focus on a single geographic location and native chicken production data, which may restrict its generalizability. Despite these limitations, the findings demonstrate that parameter optimization in SARIMA enhances forecast accuracy and can support better planning for food security initiatives.