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Rahman, Azan
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Computer Science & IT

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ANALISIS KLASIFIKASI EMAIL SPAM MENGGUNAKAN ALGORITMA NAÏVE BAYES Rahman, Azan; Maslan, Andi
Computer Science and Industrial Engineering Vol 12 No 3 (2025): Comasie Vol 12 No 3
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/comasiejournal.v12i3.9792

Abstract

Spam emails pose a significant challenge in digital communication, requiring effective classification methods to enhance cybersecurity. This study evaluates the performance of the Naïve Bayes algorithm in detecting spam emails, focusing on accuracy, precision, and recall. The dataset consists of pre-labeled emails processed using TF-IDF for feature extraction. The results indicate that the algorithm achieved an accuracy of 90% before addressing class imbalance. After applying SMOTE, the final accuracy improved to 98%. These findings demonstrate that Naïve Bayes is an effective method for spam email classification, with SMOTE enhancing its performance in handling class imbalance.
Co-Authors Maslan, Andi