Pengembangan Model Random Forest Regressor untuk Prediksi Kelembaban pada Pertanian Perkotaan Berkelanjutan

Urban Farm DB summary

This study aims to develop an IoT and AI-integrated smart farming system operating via edge computing. The prototype system collects real-time environmental data using pH, TDS, temperature, humidity, and water level sensors, processing it with the Random Forest Regressor algorithm to determine optimal conditions. Test results showed the model has very high accuracy in predicting humidity (R² = 0.99; RMSE = 0.65) and temperature (R² = 0.99; RMSE = 0.17), though discrepancies were noted under extreme conditions.