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All Journal Kubik KUBIK: Jurnal Publikasi Ilmiah Matematika
Maria Anita
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Computer Science & IT Economics, Econometrics & Finance Education Mathematics

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Perbandingan Metode Random Forest dan Naïve Bayes dalam Email Spam Filtering Maria Anita; Bambang Susanto; Lenox Larwuy
KUBIK Vol 7, No 2 (2022): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v7i2.18933

Abstract

Email is an important tool not only for communicating and transferring files but also it can be used for advertising media over the Internet. Since the increase in email user numbers, many users send viruses, fraud, and even pornography contained emails. Those kinds of emails were called spam, where unexpected emails sent in bulk. Many email users are annoyed by the amount of time spent deleting individual spam messages. This study provides a comparison between the Random Forest and Naïve Bayes classification methods for email spam predicting. It aims for searching the most accurate method. The data used in this study is an email dataset totaling 2607 data with two variables, namely the body variable (which shows the contents of the email) and the label variable (which shows labeling) where 1 indicates spam and 0 indicates not spam. From the test result using the confusion matrix, it is known that the random forest method has the highest accuracy value, namely 98%, and Naïve Bayes 73%.
Perbandingan Metode Random Forest dan Naïve Bayes dalam Email Spam Filtering Maria Anita; Bambang Susanto; Lenox Larwuy
KUBIK Vol 7, No 2 (2022): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v7i2.18933

Abstract

Email is an important tool not only for communicating and transferring files but also it can be used for advertising media over the Internet. Since the increase in email user numbers, many users send viruses, fraud, and even pornography contained emails. Those kinds of emails were called spam, where unexpected emails sent in bulk. Many email users are annoyed by the amount of time spent deleting individual spam messages. This study provides a comparison between the Random Forest and Naïve Bayes classification methods for email spam predicting. It aims for searching the most accurate method. The data used in this study is an email dataset totaling 2607 data with two variables, namely the body variable (which shows the contents of the email) and the label variable (which shows labeling) where 1 indicates spam and 0 indicates not spam. From the test result using the confusion matrix, it is known that the random forest method has the highest accuracy value, namely 98%, and Naïve Bayes 73%.
Co-Authors Bambang Susanto Laksono Trisnantoro Lenox Larwuy