Accurate SMS Spam Detection Using Support Vector Machine In Comparison With Linear Regression

Abstract

The aim of the study is to detect SMS spam using Support Vector Machine (SVM) and linear regression (LR). The dataset used in the study contains 5573 sentences, and accuracy is measured for SMS spam detection. The classification process is carried out using SVM and LR with sample sizes of N=27, which were obtained using a G-power value of 80%. The accuracy of SVM is found to be 97.67%, which is higher than LR with an accuracy of 92%. The p-value for the significant accuracy difference is 0.02 (p<0.05), indicating that SVM performs better than LR in achieving accuracy.