SISTEM PREDIKSI HARGA PANGAN DI KABUPATEN BENGKALIS BERBASIS WEB MENGGUNAKAN METODE SARIMA - Azmil Fikri; Nurmi Hidayasari; Zuliar Efendi (Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, 2026)
Fluctuations in food commodity prices such as chili, onions, and other staple commodities in Bengkalis Regency often occur unpredictably due to the influence of weather conditions, distribution, supply availability, and increased demand during certain periods, such as religious holidays. The historical daily food price data used in this study indicate the presence of weekly and annual seasonal patterns, making them suitable for modeling using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. This study aims to develop a web-based food price prediction system that is capable of presenting daily price information as well as price forecasts for the upcoming days. The data used consist of daily records from January 2022 to October 2025, obtained from the Bengkalis Regency Food Security Agency. The data were processed through data cleaning, stationarity testing, model training, and forecasting stages using the Python programming language with the statsmodels library. The system was developed using the Flask framework and a MySQL database. The results show that the SARIMA model is able to generate price forecasts that follow the trends and seasonal patterns of the historical data. Accuracy evaluation using the Root Mean Square Error (RMSE) indicates that the Minyakita commodity has an RMSE of 1,415.04, equivalent to approximately 8–9% of its average price, while commodities with high volatility, such as bird’s eye chili and imported soybeans, exhibit higher prediction errors.