Penerapan Metode Naïve Bayes Untuk Klasifikasi Sms Spam Menggunakan Java Rogramming

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pdf (Bahasa Indonesia)
Published Dec 20, 2019
DOI : https://doi.org/10.32664/j-intech.v7i02.435
  • Halaman : 104 - 108
  • Abstract views : 193
  • PDF views : 414

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Eko Ardian Pranata
STIKI Malang
Subari Subari
STIKI Malang
Go Frendi Gunawan
STIKI Malang

Abstract

Short Message Service (SMS) is one of the communication services for sending and receiving short messages in the form of text on cell phones (cellphones). SMS is still used every day because of its ease of use, simple, fast, and inexpensive. The increasing use of SMS is used by many parties to benefit, one of which is sending spam via SMS. The method used is a probabilistic approach in making inferences that is based on Bayes theorem in general. Training data used in the categorization process is obtained from journals and already has a previous category, namely SMS spam and not spam. Application in Indonesian-language SMS, which has a certain morphology in categorizing processing. The application performs several stages in processing including preprocessing in the form of case folding, and parsing, transformation in the form of stopword removal and stemming, frequency and probability calculation and naïve bayes calculation. The categorization produced by the application compared to manual categorization has an average precision of 24%, recall 88% and Confusion Matrix (Accuracy) of 62%.

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Issue
Vol 7 No 02 (2019): J-INTECH : Journal of Information and Technology
Section
Articles
References
Ali Fahnur Yavi, (2017). Klasifikasi Artikel Berbahasa Indonesia Untuk Mendeteksi Clikbait Menggunakan Metode Naïve Bayes. Malang.
Arief .2004. Spam: Dampak dan Resikonya. http://www.ebizzasia.com [25 Januari 2018]
Dewi, Ika Novita, and Catur Supriyanto. 2013. “Klasifikasi Teks Pesan Spam Menggunakan Algoritma Naïve Bayes” 2013 (November): 156–60
Feldman, R., & Sanger, J. (2007). The Text Mining Handbook Advanced Approaches in Analyzing Unstructured Data. New York: Cambridge University Press.
Ganesan, K. (2006). Text Mining, Analytics & More. Retrieved from extanalytics101.rxnlp.com:http://textanalytics101.rxnlp.com/2014/10/allabout-stop-words-for-text-mining.html
Han, J., & Kamber, M. (2006). Data Mining Concepts and Techniques.
Istiaq, A. (2014). Pasca Sarjana. Meachine Learning, 183 – 184
Kunafi, A. (2009). Klasifikasi Email Berbahasa Indonesia Menggunakan Text Mining dan Algoritma K-MEAN. Surabaya: Politeknik Elektronik Negeri Surabaya.
Natalius, Samuel. 2011. “Metoda Naïve Bayes Classifier Dan Penggunaannya Pada Klasifikasi Dokumen,” no. 3.
Pathmanaban, H. (2016). Academia. Retrivied from https://www.academia.edu/: htpps://www.academia.edu/5889117/Text_Mining
Subari, Ferdinandus. 2015. Sistem Information Retrieval Layanan Kesehatan Untuk Berobat Dengan Metode Vector Space Model (VSM) Berbasis Webgis. Malang: Sekolah Tinggi Informatika & Komputer Indonesia Malang. Vol.03
Tapen, Panji. 2008. Email Spam Filtering.http://panjitapen.wordpress.com/2008/01/27/emailspam-filtering [12 Januari 2017]
Wijaya, A. P., & Santoso, H. A. (2016). Technology. Naïve Bayes Classification pada Klasifikasi Dokumen Untuk Identifikasi Konten E-Government, 48 – 55