Deteksi Email Spam menggunakan Algoritma Convolutional Neural Network (CNN)

Chris Moulana Bachri, Wawan Gunawan

Abstract


Deteksi email spam merupakan isu penting dalam keamanan siber di Indonesia, yang menempati posisi delapan teratas di dunia dalam hal pengiriman spam. Untuk mengatasi tantangan ini, penelitian ini memperkenalkan penggunaan algoritma Convolutional Neural Network (CNN). Dengan kemampuan superior dalam mempelajari dan mengenali pola dari dataset besar, CNN menawarkan pendekatan berbasis kecerdasan buatan yang lebih efektif daripada metode tradisional. Penelitian ini mengembangkan model CNN dengan menganalisis teks dari 15.271 email berbahasa Inggris dan Indonesia dengan menggunakan teknik pembersihan teks dan Tokenization. Hasilnya menunjukkan keefektivitasan CNN yang signifikan dalam mengklasifikasikan email dengan tingkat akurasi tinggi sebesar 99.67% untuk data uji 20%, 99.64% untuk data uji 30%, dan 99.63% untuk data uji 40%. Berdasarkan hasil pengujian tersebut menunjukkan bahwa algoritma CNN berpotensi kuat dalam meningkatkan keamanan digital.


Keywords


CNN; Email Spam; Keamanan Siber; Analisis Teks; Teknik Informatika

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References


Y. Vernanda, S. Hansun, and M. B. Kristanda, “Indonesian language email spam detection using n-gram and naïve bayes algorithm,” Bull. Electr. Eng. Informatics, vol. 9, no. 5, pp. 2012–2019, 2020, doi: 10.11591/eei.v9i5.2444.

M. F. A. Kadir, A. F. A. Abidin, M. A. Mohamed, and N. A. Hamid, “Spam detection by using machine learning based binary classifier,” Indones. J. Electr. Eng. Comput. Sci., vol. 26, no. 1, pp. 310–317, 2022, doi: 10.11591/ijeecs.v26.i1.pp310-317.

Y. K. Zamil, S. A. Ali, and M. A. Naser, “Spam image email filtering using K-NN and SVM,” Int. J. Electr. Comput. Eng., vol. 9, no. 1, pp. 245–254, 2019, doi: 10.11591/ijece.v9i1.pp245-254.

A. Karim, S. Azam, B. Shanmugam, K. Kannoorpatti, and M. Alazab, “A comprehensive survey for intelligent spam email detection,” IEEE Access, vol. 7, pp. 168261–168295, 2019, doi: 10.1109/ACCESS.2019.2954791.

S. Larabi-Marie-Sainte, S. Ghouzali, T. Saba, L. Aburahmah, and R. Almohaini, “Improving spam email detection using deep recurrent neural network,” Indones. J. Electr. Eng. Comput. Sci., vol. 25, no. 3, pp. 1625–1633, 2022, doi: 10.11591/ijeecs.v25.i3.pp1625-1633.

N. Kumar, S. Sonowal, and Nishant, “Email Spam Detection Using Machine Learning Algorithms,” Proc. 2nd Int. Conf. Inven. Res. Comput. Appl. ICIRCA 2020, no. September, pp. 108–113, 2020, doi: 10.1109/ICIRCA48905.2020.9183098.

S. Huang et al., “Automatic Modulation Classification Using Compressive Convolutional Neural Network,” IEEE Access, vol. 7, pp. 79636–79643, 2019, doi: 10.1109/ACCESS.2019.2921988.

S. Kaddoura, O. Alfandi, and N. Dahmani, “A Spam Email Detection Mechanism for English Language Text Emails Using Deep Learning Approach,” Proc. Work. Enabling Technol. Infrastruct. Collab. Enterp. WETICE, vol. 2020-September, no. April 2021, pp. 193–198, 2020, doi: 10.1109/WETICE49692.2020.00045.

H. Iswanto, E. Seniwati, Y. Astuti, and D. Maulina, “Comparison of Algorithms on Machine Learning For Spam Email Classification,” IJISTECH (International J. Inf. Syst. Technol., vol. 5, no. 4, p. 446, 2021, doi: 10.30645/ijistech.v5i4.164.

Y. Fang, C. Zhang, C. Huang, L. Liu, and Y. Yang, “Phishing Email Detection Using Improved RCNN Model With Multilevel Vectors and Attention Mechanism,” IEEE Access, vol. 7, pp. 56329–56340, 2019, doi: 10.1109/ACCESS.2019.2913705.

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep Convolutional Neural Network for Inverse Problems in Imaging,” IEEE Trans. Image Process., vol. 26, no. 9, pp. 4509–4522, 2017, doi: 10.1109/TIP.2017.2713099.

B. P. B. I. C. S. Putra, “Deteksi Ujaran Kebencian Dengan Menggunakan Algoritma Convolutional Neural Network Pada Gambar,” e-Proceeding Eng. , vol. 5, no. 2, pp. 2395–2402, 2018.

I. K. M. Jais, A. R. Ismail, and S. Q. Nisa, “Adam Optimization Algorithm for Wide and Deep Neural Network,” Knowl. Eng. Data Sci., vol. 2, no. 1, p. 41, 2019, doi: 10.17977/um018v2i12019p41-46.

M. Haekal and Eliyani, “Token-based authentication using JSON Web Token on SIKASIR RESTful Web Service,” 2016 Int. Conf. Informatics Comput. ICIC 2016, no. Icic, pp. 175–179, 2017, doi: 10.1109/IAC.2016.7905711.

O. Kembuan, G. Caren Rorimpandey, and S. Milian Tompunu Tengker, “Convolutional Neural Network (CNN) for Image Classification of Indonesia Sign Language Using Tensorflow,” 2020 2nd Int. Conf. Cybern. Intell. Syst. ICORIS 2020, no. 26, 2020, doi: 10.1109/ICORIS50180.2020.9320810.

A. Theodorus, T. K. Prasetyo, R. Hartono, and D. Suhartono, “Short Message Service (SMS) Spam Filtering using Machine Learning in Bahasa Indonesia,” 3rd 2021 East Indones. Conf. Comput. Inf. Technol. EIConCIT 2021, pp. 199–202, 2021, doi: 10.1109/EIConCIT50028.2021.9431859.




DOI: http://dx.doi.org/10.26418/jp.v10i1.73306

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