Klasifikasi Argument Pada Teks dengan Menggunakan Metode Multinomial Logistic Regression Terhadap Kasus Pemindahan Ibu Kota Indonesia di Twitter

 (*)Mochammad Naufal Rizaldi Mail (Universitas Telkom, Bandung, Indonesia)
 Adiwijaya Adiwijaya (Universitas Telkom, Bandung, Indonesia)
 Said Al Faraby (Universitas Telkom, Bandung, Indonesia)

(*) Corresponding Author

Submitted: August 14, 2020; Published: October 20, 2020

DOI: http://dx.doi.org/10.30865/mib.v4i4.2348

Abstract

Information on moving the Indonesian capital from Jakarta to East Kalimantan certainly raises the pros and cons conveyed by the Indonesian people through the Twitter social network. However, the pros and cons comments are of course varied, accompanied or not accompanied by arguments or even completely unrelated to the topic under discussion. User limitations in filtering out that information will certainly make it difficult for the public or even the government to analyze the information contained in the tweet. Therefore, a system was built that could classify tweets automatically into three classes, namely non-arbitration, argument and unknown. The method used in this research is Multinomial Logistic Regression (MLR). MLR is a generalization method of Logistic Regression and is used to classify 3 or more classes. Before the classification process is carried out, the tweet must be preprocessed in order to make the tweet clear of all existing noise. Feature extractions used in this study include unigram, bigram and trigram. In this study, there are 12 test scenarios and comparison methods, namely Artificial Neural Network (ANN). Of all the test scenarios the best results for the MLR method are SRU with an accuracy of 41,30%, while for the ANN method namely the RU scenario with an accuracy of 45,10%.

Keywords


Multinomial Logistic Regression, Artificial Neural Network, Backpropagation

Full Text:

PDF


Article Metrics

Abstract View: 108 times | PDF View: 19 times

References

D. Jurafsky and J. Martin, “Logistic Regression,” Speech Lang. Process. An Introd. to Nat. Lang. Process. Comput. Linguist. Speech Recognit., p. 20, 2019.

M. Dusmanu, E. Cabrio, and S. Villata, “Argument Mining on Twitter: Arguments, Facts and Sources,” pp. 2317–2322, 2018.

M. S. Mubarok, A. Adiwijaya, and M. D. Aldhi, “Aspect-based sentiment analysis to review products using Naïve Bayes,” AIP Conf. Proc., vol. 1867, 2017.

H. N. Rohman and I. Asror, “Automatic Detection of Argument Components in Text Using Multinomial Nave Bayes Clasiffier,” in Journal of Physics: Conference Series, 2019.

T. Akhir, “Perbandingan Metode Naïve Bayes dan Metode K-Nearest Neighbor pada Klasifikasi Teks Pelamar Kerja PT . Telkom Indonesia Program Studi Sarjana Teknik Informatika Fakultas Informatika Universitas Telkom Bandung,” 2020.

P. Bafna, D. Pramod, and A. Vaidya, “Document clustering: TF-IDF approach,” Int. Conf. Electr. Electron. Optim. Tech. ICEEOT 2016, pp. 61–66, 2016.

Daeli, N.O.F. and Adiwijaya, A., 2020. Sentiment Analysis on Movie Reviews using Information Gain and K-Nearest Neighbor. Journal of Data Science and Its Applications, 3(1), pp.1-7.

D. Jurafsky and J. H. Martin, “Chapter 3: N-Gram Language Models N-Gram Language Models,” Speech Lang. Process., 2019.

T. Kim and S. J. Wright, “PMU Placement for Line Outage Identification via Multinomial Logistic Regression,” vol. 3053, no. c, 2016.

A. Bhardwaj, A. Tiwari, H. Bhardwaj, and A. Bhardwaj, “A genetically optimized neural network model for multi-class classification,” Expert Syst. Appl., vol. 60, pp. 211–221, 2016.

P. Rodríguez, M. A. Bautista, J. Gonzàlez, and S. Escalera, “Beyond one-hot encoding: Lower dimensional target embedding,” Image Vis. Comput., vol. 75, pp. 21–31, 2018.

Zhang, X., & LeCun, Y. (2017). Which encoding is the best for text classification in chinese, english, japanese and korean?. arXiv preprint arXiv:1708.02657.

Sokolova, M. and Lapalme, G., (2009). A systematic analysis of performance measures for classification tasks. Information processing & management, 45(4), pp.427-437.

Nwankpa, C., Ijomah, W., Gachagan, A. and Marshall, S., (2018). Activation functions: Comparison of trends in practice and research for deep learning. arXiv preprint arXiv:1811.03378.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Klasifikasi Argument Pada Teks dengan Menggunakan Metode Multinomial Logistic Regression Terhadap Kasus Pemindahan Ibu Kota Indonesia di Twitter

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 JURNAL MEDIA INFORMATIKA BUDIDARMA

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



JURNAL MEDIA INFORMATIKA BUDIDARMA
STMIK Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 Telp 061-7875998
email : mib.stmikbd@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.