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Comparative analysis of identifying accuracy of online misinformation of COVID-19 using SVM algorithm with decision tree classification
Alinteri Journal of Agriculture Science ; 36(1):507-512, 2021.
Article in English | GIM | ID: covidwho-1965183
ABSTRACT

Aim:

To improve the accuracy percentage of predicting misinformation about COVID-19 using SVM algorithm. Materials and

methods:

Support Vector Machine (SVM) with sample size = 20 and Decision Tree classification with sample size = 20 was iterated at different times for predicting the accuracy percentage of misinformation about COVID19. The Novel Poly kernel function used in SVM maps the dataset into higher dimensional space which helps to improve accuracy percentage. Results and

Discussion:

SVM has significantly better accuracy (94.48%) compared to Decision Tree accuracy (93%). There was a statistical significance between SVM and the Decision Tree (p=0.000) (p < 0.05 Independent Sample T-test).
Keywords

Full text: Available Collection: Databases of international organizations Database: GIM Language: English Journal: Alinteri Journal of Agriculture Science Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: GIM Language: English Journal: Alinteri Journal of Agriculture Science Year: 2021 Document Type: Article