Expert System Based on K-Nearest Neighbor for Oil Palm Fertilizer Application Optimization - Triutami, Anggun; Fakhriza, M. (Information Technology and Science (ITScience), 2025)
This study aims to develop an expert system utilizing the K-Nearest Neighbor (KNN) algorithm to recommend suitable fertilizers for oil palm plants based on soil conditions, climate, and plant age. A quantitative approach was employed, involving literature review, data collection, model development, and evaluation. Data were obtained from PT. Nusantara Plantation IV Torgamba Plantation, including variables such as soil pH, dolomite, NPK, urea application, and crop yields. The KNN model was optimized with a K-value of 6 and evaluated using metrics including accuracy (63.63%), precision, recall, F1-score, Mean Absolute Error (MAE: 1995.38), and Mean Squared Error (MSE: 5,257,254.73). The system demonstrates the ability to provide fertilizer recommendations by identifying similarities in historical data, though further accuracy improvements are possible. The practical implications of this research include assisting farmers in optimizing fertilizer selection, enhancing productivity, and minimizing environmental impact. Future studies could explore the integration of additional variables or alternative algorithms such as Decision Tree or Naive Bayes to improve performance.