Rice Planting Time Prediction Using SARIMA-MFEP Integration in Kubu Raya - Sinta Rama Dani; Syarifah Putri Agustini Alkadri; Sucipto (Politeknik Negeri Cilacap, 2026)
Extreme climate change has increased uncertainty in rice planting schedules, threatening food security in Kubu Raya Regency, West Kalimantan, and causing significant economic losses due to inaccurate seasonal predictions. This study integrates the Seasonal Autoregressive Integrated Moving Average (SARIMA) method with the Multi-Factor Evaluation Process (MFEP) to generate rice planting time recommendations based on scientific climate forecasting and multi-criteria agroclimatic evaluation. SARIMA is employed to forecast monthly rainfall, temperature, and humidity, while MFEP evaluates the feasibility of twelve alternative planting months using weighted criteria determined by local agricultural experts. The objective of this research is to develop an objective, accurate, and validated planting time prediction system to support farmers’ decision-making. The results show that the SARIMA model achieves very high accuracy, with Mean Absolute Percentage Error (MAPE) values below 2% for both temperature and humidity, and successfully captures 68% of seasonal rainfall variability. October is identified as the optimal planting month with the highest feasibility score, consistent with historical peak harvest patterns in January and February and aligned with regional literature. This integrated approach provides an end-to-end solution from forecasting to empirically validated, actionable recommendations, offering strong potential to reduce crop failure risk and enhance rice production efficiency under climate uncertainty.