PERBANDINGAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE (SVM) DALAM KLASIFIKASI SMS SPAM BERBAHASA INDONESIA
Abstract
SMS Spam (Short Message Service Spam) is an unwanted message, including advertisements and fraud. The direct impact of the spam is the inconvenience on the receiver side, therefore there is a need to have a spam screening process. One of the possible approach is to filter the spam by classifying the messages. In this paper, we compare classification performance by Naïve Bayes and Support Vector Machine (SVM). The process include preprocessing (tokenizing, stopwords removal, and stemming) from each training and testing dataset before process the information through Naïve Bayes and SVM. The results from the processing data of 1143 records (765 training and 378 testing) showed that Naïve Bayes performance surpassed SVM in term of recall (94%) and Precision (95%).
References
Juang, D. (2016). Analisis Spam dengan menggunakan Naive Bayes . Jurnal Teknovasi Volume 03, Nomor 2, 2016, 51 – 57 ISSN : 2355-701X , 51-57.
Ma, J., Zhang, Y., Liu, J., & Yu, K. (2016). Intelligent SMS Spam Filtering Using Topic Model. 2016 International Conference on Intelligent Networking and Collaborative Systems, 380-383.
Pratiwi, S., & Ulama , B. (2016). Klasifikasi Email Spam dengan Menggunakan Metode Support Vector Machine dan k-Nearest Neighbor. JURNAL SAINS DAN SENI ITS Vol. 5 No. 2 (2016) 2337-3520 (2301-928X Print) , D-344 - D-349.
Rahmayani, I. (2019, Juli Sabtu). https://kominfo.go.id/content/detail/6095. Retrieved from https://kominfo.go.id: https://kominfo.go.id/content/detail/6095/indonesia-raksasa-teknologi-digital-asia/0/sorotan_media
Rahmi, F., & Wibisono, Y. (2016, Juli Sabtu). Aplikasi SMS Spam Filtering pada Android menggunakan Naive Bayes, Unpublished manuscript. Retrieved from http://nlp.yuliadi.pro: http://nlp.yuliadi.pro/dataset
Raschka, S. (2014, Juli Sabtu). https://sebastianraschka.com/Articles. Retrieved from https://sebastianraschka.com: https://sebastianraschka.com/Articles/2014_naive_bayes_1.html
Santosa, B. (2007). Data Mining Teknik Pemanfaatan Data Untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu.
Ting, S., Ip, W., & Tsang , A. (3, July, 2011 ). Is Naïve Bayes a Good Classifier for Document Classification? International Journal of Software Engineering and Its Applications Vol. 5, No. , 37-46
In order to be accepted and published by Journal Agribest, author(s) submitting the article manuscript should complete all the review stages. By submitting the manuscript the author(s) agreed to these following terms:
- The copyright of received articles shall be assigned to Journal Agribest as the publisher of the journal. The intended copyright includes the right to publish articles in various forms (including reprints). Journal Agribest maintain the publishing rights to the published articles.
- Authors are permitted to disseminate published article by sharing the link/DOI of the article at Journal Agribest. authors are allowed to use their articles for any legal purposes deemed necessary without written permission from Journal Agribest with an acknowledgement of initial publication to this journal.
- Users/public use of this website will be licensed to Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
