Implementasi Algoritma Random Forest Regression untuk Memprediksi Hasil Panen Padi di Desa Minanga - Nahya Nur; Farid Wajidi; Sulfayanti Sulfayanti; Wildayani Wildayani (Jurnal Komputer Terapan, 2023)
Minanga Village, Bambang District, Mamasa Regency, the people of which cultivate rice plants, usually the yields fluctuate each season, which often decreases or increases unstable. This research is expected to assist in predicting rice yields in accordance with pre-existing criteria and data such as land area, number of seeds, type of fertilizer, rainfall, pests and weeds, pest and weed control, and the rice planting system used (jajar legowo) , by applying the Random Forest Regression algorithm. Evaluation of algorithm performance is measured using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and the coefficient of determination (RÂ²), the results of the Random Forest model obtained from 9 trees, the variable that has the highest value on the variable importance is the variable land area. So that from this model an accuracy value of 95.11% is obtained, the MAPE value in this model is 4.884%, the RMSE value is 0.250 and the RÂ² value is 0.99.