Lack of Efficiency in classifying Concussion of Corona Crisis over Small Businesses using Novel Support Vector Machine Algorithm Comparing K-Mode Algorithm

Abstract

Aim: The aim of this research is to classify the Concussion of Corona Crisis over Small Businesses using Novel Support Vector Machine Algorithm Comparing K-Mode Algorithm. Materials and Methods: Novel Support Vector Machine Algorithms with sample size = 110 and K-Mode with sample size = 110 with G power (value =0.6) were evaluated many times to predict the efficiency percentage. Results: Novel Support Vector Machine algorithm has better efficiency (76.70%) when compared to K-Mode algorithm efficiency (68.50%). The results achieved with significance value p=0.919 (p>0.05) shows that two groups are statistically insignificant. Conclusion: Support Vector Machine performed significantly better than the KMode algorithm.