Aim: The aim of the study is to use an efficient quality analysis of rice grains using a Novel Support Vector Machine over an K-Nearest Neighbor with improved accuracy. Materials and Methods: Novel Support Vector Machine algorithm (N=10) and K-Nearest Neighbor (N=10) was iterated 20 times and analyzed rice grains. The sample size was calculated using G-power of 80% for two groups. Results: Novel Support Vector Machine has significantly better accuracy of 67.41% compared to the K-Nearest Neighbor of 85.92%. The statistical significance of the analysis of rice grains difference is p= 0.001 (p<0.05) and Independent sample T-test value states that the results in the study are significant. Conclusion: The accuracy performance parameter of the K-Nearest Neighbor appears to be better than the Novel Support Vector Machine algorithm .