<![CDATA[This study aims to implement the Logistic Regression algorithm in spam email classification using an experimental method. Using a dataset of 4,073 emails categorized as spam and non-spam, the research involves several stages, including data preprocessing, feature extraction using the TF-IDF method, and the application of Logistic Regression for classification. The experimental evaluation of the model shows excellent performance with an accuracy of 98%, along with precision, recall, and F1-Score of 98% each. The model successfully classifies spam and non-spam emails with minimal errors, making it an effective solution for filtering unwanted emails and preventing data breaches and phishing attacks. This research demonstrates that Logistic Regression, validated through experimental analysis, is a reliable and efficient method for spam email classification and can be applied in real-world email filtering systems.]]>
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