Penerapan Text Mining dalam Spam Filtering untuk Aplikasi Chat

  • Ni Luh Ratniasih
  • Made Sudarma Universitas Udayana
  • Nyoman Gunantara Universitas Udayana

Abstract

The Internet has become something important in the communication development. One communication facilities on the Internet is the Internet relay chat or known as chat. Chat applications in real time is often misused for the purpose of spreading the virus, promotions, and other interests known as spam. Spamming is the sending of unwanted messages by someone who has a chat account. This causes the chat account feel uncomfortable with the condition. Based on these problems this research create a chat application that can filter messages or spam filtering by applying text mining. Spam filtering process can be done in two phases: text pre-processing and analyzing. These two phases are carried out to calculate the weight (W) of connectedness with the word spam messages. Based on the results of tests performed on chat applications by applying text mining to perform filtering on spam messages generate the level of accuracy of 91.41%.

Downloads

Download data is not yet available.

References

[1] Abdul Kadir & Terra CH Triwahyuni. 2003. Pengenalan Sistem Informasi. Yogyakarta: Penerbit Andi Yogyakarta.
[2] Wahyuni Diny & Susetyo Hadi. 2008. Pengembangan Aplikasi Pertukaran Pesan Berbasis Teks Melalui Jaringan Lokal (LAN) Menggunakan Microsoft Visual C++ 6.0. Jurnal Komputasi. 2008; 07.
[3] Gomez Jose Maria, Guillermo Cajigas Bringas, Enrique Puertas Sanz. 2007. Content Based SMS Spam Filtering.
[4] Kristina Paskianti. 2011. Klasifikasi Dokumen Tumbuhan Obat menggunakan Algoritma KNN Fuzzy. Thesis Fakultas Matematika dan Ilmu Pengetahuan Alam IPB. Bogor.
[5] I. H. Witten, E. Frank, and M. A. Hall. 2011. Data Mining Practical Machine Learning Tools and Technique. Burlington: Morgan Kaufmann Publisher.
[6] Pramitarini, Y., Purnama I.K.E., Purnomo, M., 2005. Analisa Rekam Medis Untuk Menentukan Status Gizi Anak Balita Menggunakan Naive Bayes Classifier. Seminar Nasional Manajemen Teknologi XVII. Surabaya. 2 Februari 2013; ISBN:978-602-97491-6-8.
[7] Feldman, R & Sanger, J. 2007. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press: New York.
[8] Weiss, S.M., Indurkhya, N., Zhang, T., Damerau, F.J. 2005. Text Mining : Predictive Methods fo Analyzing Unstructered Information. Springer: New York.
[9] Berry, M.W. & Kogan, J. 2010. Text Mining Aplication and theory. WILEY : United Kingdom.
[10] Dragut, E., Fang, F., Sistla, P., Yu, S. & Meng, W. 2009. Stop Word and Related Problems in Web Interface Integration. http://www.vldb.org/pvldb/2/vldb09-384.pdf. Diakses tanggal 8 Desember 2013.
[11] Tala, Fadillah Z. 2003. A Study of Stemming Efects on Information Retrieval in Bahasa Indonesia. Institute for Logic, Language and ComputationUniversiteit van Amsterdam The Netherlands. http://www.illc.uva.nl/Research/Reports/MoL-2003-02.text.pdf. Diakses tanggal 29 September 2014.
[12] Robertson, Stephen, Understanding Inverse Document Frequency: On theoretical arguments for IDF, Journal of Documentation, Vol. 60, pp. 502–520
Published
2017-12-29
How to Cite
RATNIASIH, Ni Luh; SUDARMA, Made; GUNANTARA, Nyoman. Penerapan Text Mining dalam Spam Filtering untuk Aplikasi Chat. Majalah Ilmiah Teknologi Elektro, [S.l.], v. 16, n. 3, p. 13-18, dec. 2017. ISSN 2503-2372. Available at: <https://ojs.unud.ac.id/index.php/jte/article/view/ID26599>. Date accessed: 08 may 2024. doi: https://doi.org/10.24843/MITE.2017.v16i03p03.

Keywords

Aplikasi Chat, Text Mining, Spam filtering.