<![CDATA[The expansion of Indonesia's transmission and distribution network increases the demand for high-voltage insulator materials with reliable dielectric performance and sustainable manufacturing. Silicone rubber (SiR) filled with coal fly ash is a promising alternative; however, its breakdown voltage varies nonlinearly with filler composition, temperature, and fly ash source. This study proposes a complementary statistical machine learning framework using Generalized Linear Mixed Effects (GLME) for interpretable statistical analysis and a Backpropagation Neural Network (BPNN) for flexible nonlinear prediction. The analysis used 512 secondary observations from four Indonesian fly ash sources, with filler compositions of 10–80% and temperatures of 35–50°C. Breakdown voltage increased with fly ash content up to an optimum of approximately 60–65% before declining at higher loadings, while increasing temperature consistently reduced dielectric strength. GLME achieved R² = 0.7457, RMSE = 0.0354, and MAPE = 1.57%, whereas the five-fold cross-validated BPNN showed slightly better average predictive performance, with R² = 0.7617, RMSE = 0.0343, and MAPE = 1.56%. GLME provides interpretable and statistically testable coefficients, whereas BPNN captures additional complex nonlinear patterns without requiring a predefined functional form. SEM and XRF characterization supported the observed nonlinear trends through particle dispersion and fly ash oxide composition, predominantly SiO₂ and Al₂O₃. The proposed complementary framework combines statistical interpretability with nonlinear predictive capability, supporting fly ash composition selection, efficient material screening, and breakdown voltage prediction for high voltage silicone rubber insulator applications.]]>
