Investigate the performance of innovative support vector machine compared over J48 decision tree algorithm on seed quality analysis with improved accuracy.

This study compares seed quality analysis using the Innovative Support Vector Machine with the J48 Decision Tree Algorithm. Two groups were evaluated: Group 1 utilized the Innovative Support Vector Machine, while Group 2 employed the J48 Decision Tree Algorithm. Each group consisted of 20 samples, totaling 40 samples overall. The accuracy assessment revealed that the Innovative Support Vector Machine model outperformed the J48 Decision Tree Algorithm, achieving a higher accuracy of 90.44% with statistical significance at the 0.043 level. Additionally, the Innovative Support Vector Machine-based feature extraction technique achieved an accuracy of 94.63%, significantly surpassing the J48 Decision Tree-based feature extraction system. [ABSTRACT FROM AUTHOR]