PERBANDINGAN PEMBOBOTAN UNTUK KLASIFIKASI TOPIK BERITA MENGGUNAKAN DECISION TREE

Main Article Content

Henri Tantyoko
Adiwijaya Adiwijaya
Untari Novia Wisesty

Abstract

News is a media to add insight into the outside world, many events that can not be known directly, because it is news that can make it easier to find out more extensive information about the increase. News dissemination consists of online for internet and offline for print media. In the present era, the development of the internet is very fast, making it easier to access information, media delivery of news becomes varied with the internet. Many news available online cause problems because news published by publishers can make mistakes in categorizing news content into the right category. Need technical contributions to categorize news automatically. Categorization of the method used. In this study, the authors used the Decision Tree classification method. A process that is no less important before classification is the word weighting technique. To get optimal accuracy, the authors combine classification techniques using Decision Tree with word weighting techniques TF.ABS, TF.CHI2, TF.RF and TF.IDF. Receive TF.ABS which has the

Article Details

How to Cite
Tantyoko, H., Adiwijaya, A., & Wisesty, U. (2019). PERBANDINGAN PEMBOBOTAN UNTUK KLASIFIKASI TOPIK BERITA MENGGUNAKAN DECISION TREE. TEKNOLOGIA, 2(1). Retrieved from https://aperti.e-journal.id/teknologia/article/view/35
Section
Articles

References

[1] I. M. R. Prawira, Adiwijaya and M. S. Mubarok, “Klasifikasi Multi-Label Pada Topik Berita Berbahasa Indonesia Menggunakan Multinomial Naïve Bayes,” vol. 5, no. 3, pp. 7774–7781, 2018.
[2] A. F. Irene, and Adiwijaya “Klasifikasi Sentimen Review Film Menggunakan Algoritma Support Vector Machine Sentiment Classification of Movie Reviews Using Algorithm Support Vector Machine,” vol. 4, no. 3, pp. 4740–4750, 2017.
[3] K. Chen, Z. Zhang, J. Long, and H. Zhang, “Turning from TF-IDF to TF-IGM for term weighting in text classification,” Expert Syst. Appl., vol. 66, pp. 1339–1351, 2016.
[4] M. A. Kurniawan, Y. Sibaroni, and K. L. Muslim, “Kategorisasi Berita Menggunakan Metode Pembobotan TF.ABS dan TF.CHI,” Indones. J. Comput., vol. 3, no. 2, p. 83, 2018.
[5] P. N. Bandung, “ANALISIS AKURASI METODE TERM WEIGHTING INDONESIA DENGAN K-NEAREST NEIGHBOR.”
[6] R. Abdul Aziz and M. Syahrul Mubarok, “Klasifikasi Topik pada Lirik Lagu dengan Metode Multinomial Naïve Bayes,” Indosc 2016, no. 1, pp. 139–148, 2016.
[7] L. A. Matsunaga and N. F. F. Ebecken, “Term weighting approaches for text categorization improving,” Proc. - 8th Int. Conf. Intell. Syst. Des. Appl. ISDA 2008, vol. 1, pp. 409–414, 2008.
[8] Man Lan, Chew Lim Tan, Jian Su, and Yue Lu, “Supervised and Traditional Term Weighting Methods for Automatic Text Categorization,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 4, pp. 721–735, 2009.
[9] F. Harrag, E. El-Qawasmeh, and P. Pichappan, “Improving Arabic text categorization using decision trees,” 2009 1st Int. Conf. Networked Digit. Technol. NDT 2009, no. August 2009, pp. 110–115, 2009.
[10] R. Wongso, F. A. Luwinda, B. C. Trisnajaya, O. Rusli, and Rudy, “News Article Text Classification in Indonesian Language,” Procedia Comput. Sci., vol. 116, pp. 137–143, 2017.
[11] R. A. Pane, M. S. Mubarok, N. S. Huda, and Adiwijaya, “A multi-lable classification on topics of Quranic verses in English translation using multinomial naive bayes,” 2018 6th Int. Conf. Inf. Commun. Technol. ICoICT 2018, no. May, pp. 481–484, 2018.
[12] G. I. Ulumudin, A. Adiwijaya, and M. S. Mubarok, “A multilabel classification on topics of qur’anic verses in English translation using K-Nearest Neighbor method with Weighted TF-IDF,” J. Phys. Conf. Ser., vol. 1192, no. 1, pp. 103–106, 2019.
[13] I. R. Ponilan, Adiwijaya, M. A. Bijaksana, and A. S. Raharusun, “Search relevant retrieval on indonesian translation hadith document using query expansion and smoothing probabilistic model,” J. Phys. Conf. Ser., vol. 1192, no. 1, 2019.
[14] F. Z. Tala, “A Study of Stemming Effects on Information Retrieval in Bahasa Indonesia,” M.Sc. Thesis, Append. D, vol. pp, pp. 39–46, 2003.