Klasifikasi Berita Bahasa Indonesia Menggunakan Mutual Information dan Support Vector Machine
DOI:
https://doi.org/10.30865/mib.v3i4.1410Abstract
News is a source of information disseminated in various types of media. In order to make it easier for news readers to obtain the desired news, the news needs to be classified. The large number of scattered news creates difficulties in classifying the news based on the topic. Therefore the author conducted a study to classify news into 12 classes (culture, economy, entertainment, law, health, life, automotive, education, politics, sports, technology, and tourism) automatically against 360 Indonesian news data. In this study several test scenarios were conducted to see the effect of stopword removal and stemming methods on data preprocessing, the effect of mutual information in selecting features, and performance of Support Vector Machine in classifying news data. The test results showed that the data using only stemming without stopword removal, using the MI selection feature and SVM classification method produced the best results of 94.24%, compared to the other methods.
References
Li-juan Zhu, Feng, Z., Qing-qing, P., Xin, Y., & Zheng-tao, Y. (2015). A classification method of Vietnamese news events based on maximum entropy model. 2015 34th Chinese Control Conference (CCC).
Rizaldy, A., & Santoso, H. A. (2017). Performance improvement of Support Vector Machine (SVM) With information gain on categorization of Indonesian news documents. 2017 International Seminar on Application for Technology of Information and Communication (iSemantic).
Dadgar, S. M. H., Araghi, M. S., & Farahani, M. M. (2016). A novel text mining approach based on TF-IDF and Support Vector Machine for news classification. 2016 IEEE International Conference on Engineering and Technology (ICETECH).
Septian, G., Susanto, A., & Shidik, G. F. (2017). Indonesian news classification based on NaBaNA. 2017 International Seminar on Application for Technology of Information and Communication (iSemantic).
Asy’arie, A. D., & Pribadi, A. W. (2009). Automatic news articles classification in Indonesian language by using Naive Bayes Classifier method. Proceedings of the 11th International Conference on Information Integration and Web-Based Applications & Services - iiWAS ’09.
Shahi, T. B., & Pant, A. K. (2018). Nepali news classification using Naïve Bayes, Support Vector Machines and Neural Networks. 2018 International Conference on Communication Information and Computing Technology (ICCICT).
Rahmawati, D., & Khodra, M. L. (2015). Automatic multilabel classification for Indonesian news articles. 2015 2nd International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA).
Purbolaksono, M. D., Widiastuti, K. C., Adiwijaya, Mubarok, M. S., & Ma’ruf, F. A. (2018, March). Implementation of mutual information and bayes theorem for classification microarray data. In Journal of Physics: Conference Series (Vol. 971, No. 1, p. 012011). IOP Publishing.
Nurfikri, Fahmi Salman, and Mohamad Syahrul Mubarok. "News Topic Classification Using Mutual Information and Bayesian Network." In 2018 6th International Conference on Information and Communication Technology (ICoICT), pp. 162-166. IEEE, 2018.
Zhili Pei, Yuxin Zhou, Lisha Liu, Lihua Wang, Yinan Lu, & Ying Kong. (2010). A mutual information and information entropy pair based feature selection method in text classification. 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).
Zareapoor, M., and Seeja, K. Feature extraction or feature selection for text classification: A case study on phishing email detection. International Journal of Information Engineering and Electronic Business 7, 2 (2015), 60.Prijono, Agus., Wijaya, Marvin Ch. (2007). Pengolahan Citra Digital Menggunakan MatLAB Image Processing Toolbox. Bandung : Informatika.
Al Faraby, S., Jasin, E.R.R. and Kusumaningrum, A., 2018, March. Classification of hadith into positive suggestion, negative suggestion, and information. In Journal of Physics: Conference Series (Vol. 971, No. 1, p. 012046). IOP Publishing.
Kuang, Q., & Xu, X. (2010). Improvement and Application of TF•IDF Method Based on Text Classification. 2010 International Conference on
Vijayan, V. K., Bindu, K. R., & Parameswaran, L. (2017). A comprehensive study of text classification algorithms. 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI).
Naf'an, M. Z., Bimantara, A. A., Larasati, A., Risondang, E. M., & Nugraha, N. A. S. (2019). Sentiment Analysis of Cyberbullying on Instagram User Comments. Journal of Data Science and Its Applications, 2(1), 88-98.
Asriyanti Indah Pratiwi, Adiwijaya. (2018). On the Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis. Applied Computational Intelligence and Soft Computing, 2018.
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