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Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
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PREDIKSI SPAM EMAIL MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN PARTICLE SWARM OPTIMIZATION
Keywords:
Support Vector Machine, Particle Swarm Optimization, Spam Email Filtering
Abstract
Spam email adalah istilah yang digunakan untuk menggambarkan pesan yang dikirim dalam email massal atau email yang masuk diterima tanpa persetujuan. Spam Email Filtering adalah program yang digunakan untuk mendeteksi email yang tidak diinginkan dan mencegah email yang tidak diminta dan untuk masuk ke inbox pengguna email. Banyak teknik yang digunakan untuk membuat email penyaringan spam, salah satunya dengan menggunakan teknik klasifikasi. Support Vector Machine classifier adalah metode pembelajaran terawasi digunakan untuk mengklasifikasikan data. Tapi Support Vector Machine memiliki kelemahan pada kesulitan memilih fitur yang tepat dan bobot atribut optimal digunakan untuk menyebabkan tingkat akurasi prediksi rendah. Oleh karena itu, dalam penelitian ini menciptakan algoritma Model dan Support Vector Machine Support Vector Machine Model algoritma berdasarkan Particle Swarm Optimization untuk mendapatkan spam email aturan dalam memprediksi akurasi dan memberikan nilai yang lebih akurat. Setelah menguji dua model, yaitu Support Vector Algoritma Mesin dan Support Vector Machine berbasis Particle Swarm Optimization, hasil yang diperoleh dengan demikian diperoleh menguji algoritma menggunakan Support Vector Machine yang merupakan nilai yang diperoleh akurasi 85,75% dan nilai AUC adalah 0,901, sedangkan Tes menggunakan Support Vector Machine berbasis Particle Swarm Optimization nilai yang diperoleh akurasi 89,24% dan nilai AUC adalah 0,935 dengan tingkat yang baik klasifikasi diagnostik. Sehingga kedua metode memiliki berbagai tingkat akurasi yaitu sebesar 03:49% dan nilai-nilai AUC 0,034 perbedaan.
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References
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http://sinaga17.wordpress.com/2014/02/26/software-anti-spamming-terbaik/
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Moertini, V. S. (2002). Data Mining Sebagai Solusi Bisnis. Integral, Vol. 7 No. 1, April 2002 , 4.
Nugroho, A. S. (2008). Support Vector Machine: Paradigma Baru Dalam Softcomputing. Konferensi Nasional Sistem Dan Informatika, 92-99.
Parimala, R., & Nallaswamy, R. (2012). Feature Selection using a Novel Particle Swarm Optimization and It’s Variants. I.J. Information Technology and Computer Science, , 16-24.
Ren, Qinqing. (2010). Feature-Fusion Framework for Spam Filtering Based on SVM.
Shukla, A., Tiwari, R., & Kala, R. (2010). Real Life Applications Of Soft Computing. New York: Taylor & Francis Group.
techterms@whatis.com,what is spam filter?, http://searchmidmarketsecurity.techtarget.com/sDefinition/0,,sid198_gci931766,00.html,midmarket IT security definition, diakses pada tanggal 20 oktober 2009.
Vercellis, C. (2009). Business Intelligence Data Mining And Optimization For Decision Making . United Kingdom: A John Wiley And Sons, Ltd., Publication.
Weiss, S. M., Indurkhya, N., & Zhang, T. (2010). Fundamentals Of Predictive Text Mining. London: Springer.
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining Practical Machine Learning Tools And Techniques. Burlington, Usa: Morgan Kaufmann Publishers.
Zhao, M., Fu, C., Ji, L., Tang, K., & Zhou, M. (2011). Feature Selection And Parameter Optimization For Support Vector Machines: A New Approach Based On Genetic Algorithm With Feature Chromosomes. School Of Computer Science And Technology , 5197–5204.
Alpaydın, E. (2010). Introduction To Machine Learning. London: Massachusetts Institute Of Technology.
Bellazzi, R., & Zupanb, B. (2008). Predictive Data Mining In Clinical Medicine: Current Issues And And Guidelines. International Journal Of Medical Informatics 7 7 , 81–97.
Berndtssom, M., Hansson, J., Olsson, B., & Lundell, B. (2008). A Guide For Students In Computer Science And Information Systems. London: Springer.
Bramer, M. (2007). Principles Of Data Mining. Verlag London: Springer.
Burges, C. J. (1998). A Tutorial On Support Vector Machines For Pattern Recognition. Boston: Kluwer Academic Publishers.
Dawson, C. W. (2009). Projects In Computing And Information System A Student's Guide. England: Addison-Wesley.
Dong, Y., Xia, Z., Tu, M., & Xing, G. (2007). An Optimization Method For Selecting Parameters In Support Vector Machines. Sixth International Conference On Machine Learning And Applications , 1.
Fei, S. W., Miao, Y. B., & Liu, C. L. (2009). Chinese Grain Production Forecasting Method Based On Particle Swarm Optimization-Based Support Vector Machine. Recent Patents On Engineering 2009 , 3, 8-12.
Gorunescu, F. (2011). Data Mining Concepts,Models And Techniques. Verlag Berlin Heidelberg: Springer.
G. Sudipto, M. Adam, M. Nina, M. Rajeev, O. Liadan, 2003, Clustering Data Streams: Theory and Practice, Radical Eye Software, pages 1-4.
Han, J., & Kamber, M. (2007). Data Mining Concepts and Techniques.
Haupt, R. L., & Haupt, S. E. (2004). Practical Genetic Algorithms. Untied States Of America: A John Wiley & Sons Inc Publication.
https://nic.itb.ac.id/mail/spam-dan-kiat-mengatasinya
http://sinaga17.wordpress.com/2014/02/26/software-anti-spamming-terbaik/
Huang, K., Yang, H., King, I., & Lyu, M. (2008). Machine Learning Modeling Data Locally And Globally. Berlin Heidelberg: Zhejiang University Press, Hangzhou And Springer-Verlag Gmbh.
Larose, D. T. (2007). Data Mining Methods And Models. New Jersey: A John Wiley & Sons.
Liu, Y., Wang, G., Chen, H., Dong, H., Zhu, X., & Wang, S. (2011). An Improved Particle Swarm Optimization for Feature Selection. Journal of Bionic Engineering Vol 8 , 1-10.
Maimon, O. (2010). Data Mining And Knowledge Discovery Handbook. New York Dordrecht Heidelberg London: Springer.
Moertini, V. S. (2002). Data Mining Sebagai Solusi Bisnis. Integral, Vol. 7 No. 1, April 2002 , 4.
Nugroho, A. S. (2008). Support Vector Machine: Paradigma Baru Dalam Softcomputing. Konferensi Nasional Sistem Dan Informatika, 92-99.
Parimala, R., & Nallaswamy, R. (2012). Feature Selection using a Novel Particle Swarm Optimization and It’s Variants. I.J. Information Technology and Computer Science, , 16-24.
Ren, Qinqing. (2010). Feature-Fusion Framework for Spam Filtering Based on SVM.
Shukla, A., Tiwari, R., & Kala, R. (2010). Real Life Applications Of Soft Computing. New York: Taylor & Francis Group.
techterms@whatis.com,what is spam filter?, http://searchmidmarketsecurity.techtarget.com/sDefinition/0,,sid198_gci931766,00.html,midmarket IT security definition, diakses pada tanggal 20 oktober 2009.
Vercellis, C. (2009). Business Intelligence Data Mining And Optimization For Decision Making . United Kingdom: A John Wiley And Sons, Ltd., Publication.
Weiss, S. M., Indurkhya, N., & Zhang, T. (2010). Fundamentals Of Predictive Text Mining. London: Springer.
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining Practical Machine Learning Tools And Techniques. Burlington, Usa: Morgan Kaufmann Publishers.
Zhao, M., Fu, C., Ji, L., Tang, K., & Zhou, M. (2011). Feature Selection And Parameter Optimization For Support Vector Machines: A New Approach Based On Genetic Algorithm With Feature Chromosomes. School Of Computer Science And Technology , 5197–5204.
Published
2016-09-15
How to Cite
Pudjiarti, E. (2016). PREDIKSI SPAM EMAIL MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN PARTICLE SWARM OPTIMIZATION. Jurnal Pilar Nusa Mandiri, 12(2), 171-181. Retrieved from https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/271
Section
Articles
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