Development of an Agricultural Department Application to Predict Small Chili Prices - Utomo, Muhammad Nur Yasir; Nur, Rini; Maemunah, Maemunah (Jurnal Teknologi Elekterika, 2024)
Small chili farmers in Soppeng Regency face challenges in planning production and marketing due to fluctuating prices. To assist various stakeholders, an accurate price prediction system is needed. This research proposes a system solution to predict small chili prices using the Gradient Boosting Algorithm. The Gradient Boosting Algorithm is employed to predict prices based on historical data using tools such as Visual Studio Code, Microsoft Excel, Python, PHP, CSS, and SQL. The dataset used consists of chili price data provided by the Department of Agriculture of Soppeng Regency, covering prices from 2018 to April 2024. The developed prediction system is capable of providing accurate price predictions, aiding farmers, traders, and consumers in production and purchasing planning. This algorithm is highly effective in predicting small chili prices and offers significant benefits to farmers, traders, and consumers in the region. The system was tested using Mean Absolute Error (MAE) and User Acceptance Testing (UAT). The results showed that the prediction error, measured using MAE, was 2730.83. Meanwhile, the User Acceptance Testing yielded a questionnaire score of 86.27%, indicating that the system has excellent performance and is highly suitable for use. It is hoped that this system will provide benefits to the entire community, especially small chili farmers, in production planning.