Spam Detection Using Radial Base Function and Compare the Prediction Accuracy Using Naive Bayes Algorithm

The proposed study aims to detect spam by utilizing the Radial Base Function and comparing the accuracy of predictions with Naive Bayes. Using machine learning techniques like Naive Bayes and Radial Base Function, spam can be detected. In this case, gpower 80% was used for the power analysis, and there are 20 participants in each of the two groups. Spam was detected with an accuracy of 0.93% and 0.96% using the Radial Base Function and Naive Bayes, respectively. To ascertain statistically significant differences, an independent sample t-test was employed, yielding a 2-tailed accuracy of 0.002 (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p&lt;0.05$</tex>). Radial Base Function is outperformed by Naive Bayes.