AN INTERACTIVE SPAM DETECTION MODEL USING AN ENSEMBLE ALGORITHIM
DOI:
https://doi.org/10.46754/jmsi.2026.06.007Keywords:
Spam messages, SMS, preprocessing, TF-IDF, feature extractionAbstract
Short Message Service (SMS) spam remains a significant issue in mobile communication systems. This study presents a framework for identifying spam messages in SMS communications using an ensemble of machine learning techniques for classification. The proposed framework involves a data preprocessing procedure to prepare the raw SMS dataset, followed by feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) technique to represent textual data numerically. This enables the model to capture relevant characteristics from the processed data. During this experiment, the Ensemble model achieved 97% accuracy, outperforming Support Vector Machine (SVM) K-Nearest Neighbour (KNN), and Random Forest (RF), which achieved accuracies of 70.93%, 73.21%, and 79.18%, respectively. The proposed approach enhances user security in mobile communication and provides a comprehensive solution to the persistent issue of SMS spam, providing an effective spam detection system.
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