IMPLEMENTASI ALGORITMA MULTINOMIAL NAIVE BAYES CLASSIFIER

Anif Hanifa Setianingrum, Dea Herwinda Kalokasari, Imam Marzuki Shofi

Abstract


ABSTRAK

Informasi diperkirakan lebih dari 80% tersimpan dalam bentuk teks tidak terstruktur. Oleh karena itu, dibutuhkan sistem pengelolaan teks yaitu dengan metode text mining yang diyakini memiliki potensial nilai komersial tinggi. Salah satu implementasi dari text mining yaitu klasifikasi teks. Tidak hanya dokumen, pemanfaatan klasifikasi juga digunakan pada surat. Peneliti mengkaji Multinomial Naive Bayes Classifier untuk mengklasifikasi surat keluar sehingga dapat menentukan nomor surat secara otomatis. Sistem klasifikasi didukung dengan confix-stripping stemmer untuk menemukan kata dasar dan TF-IDF untuk pembobotan kata. Pengujian diukur dengan menggunakan confusion matrix. Dari hasil pengujian menunjukkan bahwa implementasi Multinomial Naive Bayes Classifier pada sistem klasifikasi surat memiliki tingkat accuracy, precision, recall, dan F-measure berturut-turut sebesar 89,58%, 79,17%, 78,72%, dan 77,05%.

 

ABSTRACT

The information estimated that more than 80% is stored in the form of unstructured text. Therefore, it takes a text management system, namely text mining method is believed to have high potential commercial. One of text mining implementation is text classification. Not only documents, the use of classification is also used in official letter. Researcher examined Multinomial Naive Bayes Classifier to classify the letter so it can determine the letters classification code automatically. The classification system is supported by confix-stripping stemmer to find root and TF-IDF for term weighting. The test used by confusion matrix of a classified as a measure of its quality. The test results showed that the implementation of Multinomial Naive Bayes Classifier on letter classification system has a level of accuracy, precision, recall, and F-measure respectively for 89.58%, 79.17%, 78.72% and 77.05%.

How to Cite : Setianingrum, A. H. Kalokasari, D.H . Shofi. I. M. (2017). IMPLEMENTASI ALGORITMA MULTINOMIAL NAIVE BAYES CLASSIFIER. Jurnal Teknik Informatika, 10(2), 109-118. doi: 10.15408/jti.v10i2.6822

Permalink/DOI: http://dx.doi.org/10.15408/jti.v10i2.6822


Keywords


Surat Keluar, Klasifikasi, Text Mining, Multinomial Naive Bayes Classifier, Confix-stripping Stemmer, TF-IDF

Full Text:

PDF

References


C. Bridge. 2011. "Unstructured Data and the 80 Percent Rule.," [Online]. Available: https://breakthroughanalysis.com/2008/08/01/unstructured-data-and-the-80-percent-rule/. [Accessed 20 April 2016].

A. P. Wijaya. 2016. "Klasifikasi Dokumen dengan Naive Bayes Classifier (NBC) untuk Mengetahui Konten E-Goverment," Journal of Applied Intelligent System, Vol.1, No. 1, pp. 48-55,

S. Wijaya. 2009. Surat-Surat Kesekretariatan, Jakarta: Pustaka Grahatama

R. Feldman and J. Sanger. 2007. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data, New York: Cambridge University Press.

C. C. Aggarwal and C. Zhai. 2012. An Introduction to text mining, New York: Springer

S. Andini. 2013. "Klasifikasi Dokumen Teks menggunakan Algoritma Naive Bayes dengan Bahasa Pemograman Java," Jurnal Teknologi Informasi dan Pendidikan, Vol 6 no.2, pp. 140 - 147,

M. Adriani, J. Asian, B. Nazief, S. Tahaghoghi and H. Williams. 2007. "Stemming Indonesian : A Confix-Stripping Approach.," Transactions on Asian Language Information Processing, Vol. 6, No.4

B. Kurniawan, S. Effendi and O. S. Sitompul. 2012. "Klasifikasi Konten Berita dengan Metode Text Mining," Jurnal Dunia Teknologi Informasi, Vol. 1, pp. 14-19

I. H. Witten, F. Eibe and M. A. Hall. 2011. Data mining : Practical Machine Learning Tools and Techniques. Third Edition, USA: Elsevier

M. W. Berry and J. Kogan. 2010. Text Mining Application and Theory, United Kingdom: John Wiley and Sons

B. Liu. 2011. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Second Edition, New York: Spinger

P. Cichosz. 2014. Data Mining Algorithms: Explained Using R, Chichester: John Wiley & Sons




DOI: https://doi.org/10.15408/jti.v10i2.6822 Abstract - 0 PDF - 0

Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 Prodi Teknik Informatika Universitas Islam Negeri Syarif Hidayatullah Jakarta

Lantai 3, Prodi Teknik Informatika, UIN Syarif Hidayatullah Jakarta
Jl. Ir. H. Juanda No.95, Cempaka Putih, Ciputat Timur. 
Kota Tangerang Selatan, Banten 15412
Tlp/Fax: +62 21 74019 25/ +62 749 3315
Handphone: +6281371798903
E-mail: jurnal-ti@uinjkt.ac.id


Creative Commons Licence
Jurnal Teknik Informatika by Prodi Teknik Informatika Universitas Islam Negeri Syarif Hidayatullah Jakarta is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at http://journal.uinjkt.ac.id/index.php/ti.

 

JTI Visitor Counter: View JTI Stats

 Flag Counter