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All Journal Jurnal Pilar Nusa Mandiri
Hilman F Pardede
Pusat Penelitian Infomatika LIPI & STMIK Nusa Mandiri
Computer Science & IT

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KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE Agus Setiyono; Hilman F Pardede
Jurnal Pilar Nusa Mandiri Vol 15 No 2 (2019): PILAR Periode September 2019
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (736.163 KB) | DOI: 10.33480/pilar.v15i2.693

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.
Co-Authors Agus Setiyono