DETEKSI EMAIL SPAM MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN)

BACHRI, CHRIS MOULANA (2024) DETEKSI EMAIL SPAM MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN). S1 thesis, Universitas Mercu Buana Jakarta.

	Text (HAL COVER) 1. Cover.pdf Download (462kB)
Preview	Text (ABSTRAK) 2. Abstrak.pdf Download (75kB) | Preview
	Text (BAB I) 3. BAB 1.pdf Restricted to Registered users only Download (114kB)
	Text (BAB II) 4. BAB 2.pdf Restricted to Registered users only Download (335kB)
	Text (BAB III) 5. BAB 3.pdf Restricted to Registered users only Download (515kB)
	Text (BAB IV) 6. BAB 4.pdf Restricted to Registered users only Download (1MB)
	Text (BAB V) 7. BAB 5.pdf Restricted to Registered users only Download (84kB)
	Text (DAFTAR PUSTAKA) 8. Daftar Pustaka.pdf Restricted to Registered users only Download (111kB)
	Text (LAMPIRAN) 9. Lampiran.pdf Restricted to Registered users only Download (994kB)

Abstract

This study aims to develop a spam email detection system utilizing the Convolutional Neural Network (CNN) algorithm. The research involved analyzing the text of 5,000 emails in both English and Indonesian to distinguish spam characteristics. The CNN method was employed with data processing that includes text cleansing and tokenization. The results show that the CNN model is effective with high accuracy in classifying emails, proving its potential as a solution for digital security. Keywords: CNN, Email Spam, Computer Science, Text Analysis, Cybersecurity Penelitian ini mengembangkan sistem deteksi email spam berbasis algoritma Convolutional Neural Network (CNN). Penelitian melibatkan analisis teks dari 5000 email berbahasa Inggris dan Indonesia untuk membedakan ciri spam. Metode yang digunakan adalah CNN dengan pengolahan data meliputi pembersihan teks dan Tokenization. Hasil menunjukkan model CNN efektif dengan akurasi tinggi dalam mengklasifikasikan email, membuktikan potensinya sebagai solusi keamanan digital. Kata Kunci: CNN, Email Spam, Teknik Informatika, Analisis Teks, Keamanan Siber.

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 24 045
Call Number:	SIK/15/24/038
NIM/NIDN Creators:	41517110052
Uncontrolled Keywords:	CNN, Email Spam, Teknik Informatika, Analisis Teks, Keamanan Siber
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika > 004.6 Interfacing and Communications/Tampilan Antar Muka (Interface) dan Jaringan Komunikasi Komputer 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika > 004.6 Interfacing and Communications/Tampilan Antar Muka (Interface) dan Jaringan Komunikasi Komputer > 004.69 Specific Kinds of Computer Communications/Jenis Khusus Komunikasi Komputer > 004.692 Electronic Mail, Email/Surat Elektronik, Surel 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.32 Neural Nets (Neural Network)/Jaringan Saraf Buatan 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	23 Feb 2024 05:58
Last Modified:	23 Feb 2024 05:58
URI:	http://repository.mercubuana.ac.id/id/eprint/86464

Actions (login required)

View Item