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All Journal dCartesian: Jurnal Matematika dan Aplikasi Media Statistika Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Kubik Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Ekonomi dan Studi Pembangunan (Journal of Economics and Development Studies) Jurnal Mercumatika : Jurnal Penelitian Matematika dan Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Abdi Insani Indonesian Journal of Data and Science Jurnal Sains dan Edukasi Sains SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) Jurnal Pendidikan JAMBURA JOURNAL OF PROBABILITY AND STATISTICS ADPEBI International Journal of Business and Social Science Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Akuntansi dan Keuangan Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Jurnal Pendidikan Indonesia (Japendi) Jurnal Kedokteran STM (Sains dan Teknologi Medik) Eduvest - Journal of Universal Studies Multifinance KISA INSTITUE : Journal of Economics, Accounting, Business, Management, Engineering and Society Adpebi International Journal of Multidisciplinary Sciences d'Cartesian: Jurnal Matematika dan Aplikasi SJME (Supremum Journal of Mathematics Education)
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Journal : Kubik

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%.