KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

  • Agus Setiyono STMIK Nusa Mandiri
  • Hilman F Pardede Pusat Penelitian Infomatika LIPI & STMIK Nusa Mandiri
Keywords: Multinomial Naive Bayes, SMS Spam Detection, Support Vector Machine

Abstract

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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Published
2019-09-08
How to Cite
Setiyono, A., & Pardede, H. (2019). KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE. Jurnal Pilar Nusa Mandiri, 15(2), 275-280. https://doi.org/10.33480/pilar.v15i2.693
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