Prediction of Rice Harvest Yields Using the ARIMA Algorithm at the Agricultural Extension Center
DOI:
https://doi.org/10.30871/jaic.v10i2.12220Keywords:
ARIMA, Prediction, Rice Yield, ADF test, MAPEAbstract
Rice production plays a crucial role in supporting regional food security; therefore, accurate forecasting is essential for effective agricultural planning. This study aims to forecast rice yields in Meurah Mulia District using a univariate Autoregressive Integrated Moving Average (ARIMA) model based on annual data from 2015 to 2024 obtained from the Agricultural Extension Agency. The modeling process includes stationarity testing using the Augmented Dickey–Fuller (ADF) test, model selection using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), and residual diagnostics using the Ljung–Box test. The selected ARIMA model generates one-step-ahead forecasts for 2025 across 48 villages, with predicted yields ranging from 130.19 tons (Pri Ketapang) to 671.83 tons (Ulee Meuria), reflecting heterogeneous production patterns among villages. Model accuracy is evaluated using the Mean Absolute Percentage Error (MAPE), with values below 2% across all villages,indicating satisfactory in-sample forecasting performance. However, this study applies a univariate ARIMA approach; therefore, external variables are not incorporated. The findings provide preliminary insights to support agricultural planning, while further research is recommended to enhance model robustness and generalizability.
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