Sistem Deteksi Surel SPAM Dengan DNSBL Dan Support Vector Machine Pada Penyedia Layanan Mail Marketing
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Fahri Firdausillah
*
Universitas Dian Nuswantoro, Semarang,
Indonesia
- Muhammad Hafidz Universitas Dian Nuswantoro, Semarang, Indonesia
- Erika Devi Udayanti Universitas Dian Nuswantoro, Semarang, Indonesia
- Etika Kartikadarma Universitas Dian Nuswantoro, Semarang, Indonesia (*) Corresponding Author
Abstract
Mail marketing is an effective communication medium for users and internet providers. Many companies use email as a mean of communication with customers to ensure customers are not left behind with the latest information, and at once provide personalized offers to specific customers. However, not all emails that are sent reach mail inbox as expected. There are several factors as the cause including content that does not comply with the writing rules and tends to have SPAM signatures, invalid e-mail addresses, the sender domains are registered in the blacklist and so forth. Mail marketing service providers such as MTarget and Mailchimp must ensure that emails sent by their customers have no potential to become spam, because it can affect all of their mail marketing services will be blacklisted, thus promotional goals will not be achieved. In that case, a system is needed to check the e-mail that will be sent by the customer, to ensure that the e-mail will not detected as a spam by email service applications such as Gmail. This research produces an email validator system that can prevent sending emails that have the potential to become SPAM, so as to reduce the risk of a mail marketing service provider being blacklisted which results in delays in promotion via email and a decrease in marketing turnover. The proposed method used in this research is the Domain Name System-Based Blackhole List (DNSBL) to check the IP and the sending domain and the Support Vector Machine (SVM) to check the content of the email to be sent. The system developed has been functioning as expected and has an accuracy rate of 97.54% in detecting SPAM emails.
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References
M. Hartemo, “Email marketing in the era of the empowered consumer,” J. Res. Interact. Mark., 2016.
N. S. Sahni, S. C. Wheeler, and P. Chintagunta, “Personalization in email marketing: The role of noninformative advertising content,” Mark. Sci., vol. 37, no. 2, pp. 236–258, 2018.
D. S. Silnov, “An analysis of modern approaches to the delivery of unwanted emails (spam),” Indian J. Sci. Technol., vol. 9, no. 4, pp. 1–4, 2016, doi: 10.17485/ijst/2016/v9i4/84803.
H. S. Alkahtani, P. Gardner-Stephen, and R. Goodwin, “A TAXONOMY OF EMAIL SPAM FILTERS,” p. 6.
A. Al Mugni, M. F. Herdiansah, M. G. Andhika, and M. Ridwan, “DNSBL for internet content filtering utilizing pfsense as the next generation of opensource firewall,” in 2019 6th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2019, pp. 117–121.
E. Blanzieri and A. Bryl, “A survey of learning-based techniques of email spam filtering,” Artif. Intell. Rev., vol. 29, no. 1, pp. 63–92, 2008.
S. Suthaharan, “Support vector machine,” in Machine learning models and algorithms for big data classification, Springer, 2016, pp. 207–235.
A. N. Pour, “Minimizing the Time of Spam Mail Detection by Relocating Filtering System to the Sender Mail Server,” Int. J. Netw. Secur. Its Appl., vol. 4, no. 2, pp. 53–62, Mar. 2012, doi: 10.5121/ijnsa.2012.4204.
M. B. Firdaus, I. M. Patulak, A. Tejawati, A. Bryantama, G. M. Putra, and H. S. Pakpahan, “Agile-scrum software development monitoring system,” in 2019 International Conference on Electrical, Electronics and Information Engineering (ICEEIE), 2019, vol. 6, pp. 288–293.
F. M. Fowler, “Scrum artifacts,” in Navigating Hybrid Scrum Environments, Springer, 2019, pp. 55–57.
F. Dobrigkeit, D. de Paula, and M. Uflacker, “InnoDev: a software development methodology integrating design thinking, scrum and lean startup,” in Design Thinking Research, Springer, 2019, pp. 199–227.
M. Fejrskov, J. M. Pedersen, and E. Vasilomanolakis, “Using NetFlow to Measure the Impact of Deploying DNS-based Blacklists,” in Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 2021, vol. 398 LNICST, pp. 476–496. doi: 10.1007/978-3-030-90019-9_24.
D. Pandya, “Spam Detection Using Clustering-Based SVM,” in Proceedings of the 2019 2nd International Conference on Machine Learning and Machine Intelligence, 2019, pp. 12–15.
A. W. Haryanto, E. K. Mawardi, and others, “Influence of word normalization and chi-squared feature selection on support vector machine (svm) text classification,” in 2018 International Seminar on Application for Technology of Information and Communication, 2018, pp. 229–233.
S. M. H. Dadgar, M. S. Araghi, and M. M. Farahani, “A novel text mining approach based on TF-IDF and Support Vector Machine for news classification,” in 2016 IEEE International Conference on Engineering and Technology (ICETECH), 2016, pp. 112–116.
C. Wiecher, J. Greenyer, and J. Korte, “Test-driven scenario specification of automotive software components,” in 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), 2019, pp. 12–17.
S. Yadav and S. Shukla, “Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification,” in 2016 IEEE 6th International conference on advanced computing (IACC), 2016, pp. 78–83.
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Copyright (c) 2022 Fahri Firdausillah, Muhammad Hafidz, Erika Devi Udayanti, Etika Kartikadarma
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