This study aimed to optimize the removal of Cu(II) ions from an aqueous solution using a Jatropha oil bio-based membrane blended with 0.50 wt% graphene oxide (JPU/GO 0.50 wt%) using a central composite model (CCD) design using response surface methodology. The input factors were the feed concentration (60–140) ppm, pressure (1.5–2.5) bar, and solution pH value (3–5). An optimum Cu(II) ions removal of 87% was predicted at 116 ppm feed concentration, 1.5 bar pressure, and pH 3.7, while the validated experimental result recorded 80% Cu(II) ions removal, with 95% of prediction intervals. A statistically non-significant term was removed from the analysis by the backward elimination method to improve the model’s accuracy. Using the reduction method, the predicted R2 value was increased from −0.16 (−16%) to 0.88 (88%), suggesting that the reduced model had a good predictive ability. The quadratic regression model was significant (R2 = 0.98) for the optimization prediction. Therefore, the results from the reduction model implied acceptable membrane performance, offering a better process optimization for Cu(II) ions removal.