The Development of Classification Algorithm Models on Spam SMS Using Feature Selection and SMOTE

Short Message Service (SMS) is a widely used communication media. Unfortunately, the increasing usage of SMS has resulted in the emergence of SMS spam, which often disturbs the comfort of cellphone users. Developing a classification model as a solution for filtering SMS spam is very important to minimize disruption and loss to cellphone users due to SMS spam. To address this issue, utilize the Naïve Bayes algorithm and Support Vector Machine (SVM) along with Chi-square and Information Gain. This study focuses on the classification and analysis of SMS spam on a cellular operator service in a telecommunications company using machine learning techniques. This study applies and combines a combination of classification methods including Naive Bayes and Support Vector Machine (SVM). The combination is carried out with Chi-square and Information Gain feature selection to reduce irrelevant features. This study also applies a combination with data balancing techniques using the Synthetic Minority Oversampling Technique (SMOTE) to balance the number of unbalanced classes. The results show that SMOTE improves classification performance. SVM performs spam SMS classification or not spam Model 7 (SVM) achieves accuracy 98,55% and it has improved the performance when it was combined with SMOTE Model 10 (SVM + SMOTE) achieves F1-score 99,23% in performing spam SMS classification or not this outperforms all other models. These results indicate that the SVM algorithm achieved better performance in detecting spam SMS compared to Naive Bayes, which demonstrated a lower level of accuracy. These results illustrate the effectiveness of combining machine learning models to enhance classification accuracy with balanced data, emphasizing the model that exhibited the most substantial improvement in performance.