PREDICTING THE ACCURACY OF APPRECIATIVE USER NATURE ANALYSIS BASED ON SOCIAL MEDIA SURVEY USING NOVEL DECISION TREE ALGORITHM COMPARING SVM ALGORITHM

Aim: The main aim of this research is to compare the novel Decision Tree algorithm with SVM algorithm in order to estimate the accuracy of an user nature analysis based on social media networks. Materials and Methods: The novel Decision Tree with sample size = 10 and SVM with sample size = 10 algorithms were used to classify the data, and the results were compared based on their accuracy. A collection of records was used to implement and test the novel Decision Tree Algorithm and SVM Algorithm for novel nonobservance users nature analysis. Results and Discussion: The accuracy of Decision Tree algorithm is 92.05% obtained when compared to SVM Algorithm is 90.55%for user nature analysis on social media. The Decision Tree algorithm performed significantly better than the SVM algorithm with a G power of 80%. The statistical significance difference is p=0.007 (p<0.05) value and it states that the results in this research are significant. Conclusion: The comparison results show that the Decision Algorithm shows better accuracy than the SVM algorithm.