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All Journal Jurnal Informatika dan Teknik Elektro Terapan
Firmansyah, Faiz Agil
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Computer Science & IT

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PENERAPAN ALGORITMA NAIVE BAYES DENGAN CHI-SQUARE UNTUK KLASIFIKASI SPAM EMAIL BERBASIS KATA DAN FREKUENSI Firmansyah, Faiz Agil; Enri, Ultach; Maulana, Iqbal
Jurnal Informatika dan Teknik Elektro Terapan Vol 13, No 1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i1.5506

Abstract

Email has become an essential communication tool in everyday life. However, the ease of its use is also exploited by irresponsible parties to spread spam. This research aims to implement the Naive Bayes algorithm with Chi-square in classifying spam emails based on words and frequency. The dataset used in this research consists of 153 data. This data was processed using the classification method using the Naive Bayes algorithm with Chi-square through the Knowledge Discovery in Databases (KDD) process. The results show that the accuracy value is 81.00%, the precision value is 100%, the recall value is 65%, and the F1-score value is 79% using Naive Bayes with Chi-square. Furthermore, the evaluation results using the ROC curve show that the AUC value reaches 0.91, which is categorized as very good. This research shows that the Naive Bayes algorithm with Chi-square is successful in classifying spam emails based on words and frequency.
PENERAPAN ALGORITMA NAIVE BAYES DENGAN CHI-SQUARE UNTUK KLASIFIKASI SPAM EMAIL BERBASIS KATA DAN FREKUENSI Firmansyah, Faiz Agil; Enri, Ultach; Maulana, Iqbal
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i1.5506

Abstract

Email has become an essential communication tool in everyday life. However, the ease of its use is also exploited by irresponsible parties to spread spam. This research aims to implement the Naive Bayes algorithm with Chi-square in classifying spam emails based on words and frequency. The dataset used in this research consists of 153 data. This data was processed using the classification method using the Naive Bayes algorithm with Chi-square through the Knowledge Discovery in Databases (KDD) process. The results show that the accuracy value is 81.00%, the precision value is 100%, the recall value is 65%, and the F1-score value is 79% using Naive Bayes with Chi-square. Furthermore, the evaluation results using the ROC curve show that the AUC value reaches 0.91, which is categorized as very good. This research shows that the Naive Bayes algorithm with Chi-square is successful in classifying spam emails based on words and frequency.
Co-Authors Enri, Ultach Iqbal Maulana