Rainfall Prediction with Radial Basis Function Neural Network and Its Correlation with Bird's Eye Chili (Capsicum frutescens) Production in Sawangan Subdistrict Magelang Regency - Azka Sinatrya; Bayu Dwi Apri Nugroho; Chandra Setyawan (The University of Lampung, 2026)
Climate change causes rainfall anomalies that directly impact the decline in horticultural crop productivity, particularly bird's eye chili (Capsicum frutescens). This study aimed to analyze the effect of global rainfall indices in Sawangan Subdistrict through the development a prediction model. Modeling was performed using Radial Basis Function Neural Network (RBFNN) method with Principal Component Analysis (PCA) integration to simplify the climate index input variables. Model accuracy was evaluated using the Root Mean Squared Error (RMSE), Nash-Sutcliffe Efficiency (NSE), and R2. Furthermore, the prediction results were correlated with chili production data to test the relevance of the model to actual conditions. Results showed that model configurations provide varying performance. The best model based on evaluation is the model in the 15-year range using PCA 3 global climate indices and training percentage of 90% (RMSE: 101.39; NSE: 0.7268). However, for validation and correlation with production, it was found that the 15-year range using PCA 5 global climate indices and training percentage of 70% was the best model with highest R2 value of 0.8572 and correlation value close to actual value. Variations in data period, number of climate indices, and training data proportion affect model performance. Adding data volume and variable complexity does not always improve accuracy, so it is necessary to identify the optimum point to get the most reliable prediction model.