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All Journal Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) 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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Perancangan Sistem Informasi Pembayaran Sekolah Berbasis Java Netbeans di SMP Ar-Rahmah Jonggol Syafiq Al Ramadhan; Halimatus Sa'diah; Zikriah Zikriah
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol 4, No 03 (2023): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v4i03.4430

Abstract

Tujuan Penulisan adalah merancang sebuah sistem yang terkomputerisasi untuk mempermudah dan akurat dalam informasi pembayaran sekolah di SMP Ar-Rahmah Jonggol, mempermudah dalam pengolahan data sehingga jika dibutuhkan sewaktu-waktu akan siap sedia, Mempermudah dalam menyelesaikan laporan. Peneliti mengunakan metode penelitian deskriptif kualitatif. Penelitian ini merupakan pengumpulan data dan informasi mengenai keadaan serta gejala yang ada yaitu keadaan gejala yang apa adanya pada saat melakukan penelitian dilakukan. Peneliti membuat rancangan sistem dengan bahasa pemograman java dan netbeans serta xampp
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 Syafiq Al Ramadhan Zikriah Zikriah