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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Halimatus Sa'diah
Universitas Indraprasta PGRI
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Application of Deep Learning for Email Spam Detection Using Artificial Neural Network Dewi Leyla Rahmah; Irnawati; Dewi Mustari; Bertha Meyke Waty Hutajulu; Halimatus Sa'diah; Siti Julaeha
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.1086

Abstract

The rapid development of digital communication technology has significantly increased the use of email, followed by the growing threat of spam emails that may disrupt user security and convenience. Spam emails are commonly used for advertisements, phishing attacks, and malware distribution, potentially causing financial losses and data theft. This study aims to implement a Deep Learning method based on Artificial Neural Network (ANN) to automatically detect spam emails and analyze the model performance using classification evaluation parameters. The research employed a quantitative experimental approach using a dataset of 10,000 emails consisting of spam and non-spam categories. The research stages included data preprocessing, text transformation using TF-IDF, ANN model training, system testing, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results showed that the ANN model achieved an accuracy of 96.4%, precision of 95.9%, recall of 96.7%, and F1-score of 96.3%. In addition, the pre-test and post-test results indicated a performance improvement of more than 11% after implementing the Deep Learning method. Based on these findings, the ANN method proved effective in improving the performance of spam email detection systems and can be utilized as a solution to support digital communication security more effectively.
Co-Authors Bertha Meyke Waty Hutajulu Dewi Leyla Rahmah Dewi Mustari Irnawati Siti Julaeha