A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification


Styawati Styawati(1*), Khabib Mustofa(2)

(1) Department of Information System, FTIK Universitas Teknokrat Indonesia, Lampung
(2) Departement of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author


The sentiment analysis used in this study is the process of classifying text into two classes, namely negative and positive classes. The classification method used is Support Vector Machine (SVM). The successful classification of the SVM method depends on the soft margin coefficient C, as well as the σ parameter of the kernel function. Therefore we need a combination of SVM parameters that are appropriate for classifying film opinion data using the SVM method. This study uses the Firefly method as an SVM parameter optimization method. The dataset used in this study is public opinion data on several films. The results of this study indicate that the Firefly Algorithm (FA) can be used to find optimal parameters in the SVM classifier. This is evidenced by the results of SVM system testing using 2179 data with nine SVM parameter combinations resulting in 85% highest accuracy, while the FA-SVM system with nine population and generation combinations produces the highest accuracy of 88%. The second test results using 1200 data using the same combination as the one test, the SVM method produces the highest accuracy of 87%, while the FA-SVM method produces the highest accuracy of 89%.


Optimization; Classification; SVM; FA-SVM

Full Text:



[1]      M. H. WAHYUDI, “Klasifikasi Spermatozoa Sapi Pembawa Kromosom X atau Y Dengan Menggunakan The Classification Of OX’S Spermatozoa Carries X or Y Chromosomes Using Support Vector Machine,” Institut Teknologi Sepuluh Nopember, 2015.

[2]      Y. Fei, “Simultaneous Support Vector Selection and Parameter Optimization Using Support Vector Machines for Sentiment Classification,” IEEE, pp. 59–62, 2016.

[3]      K. N. J. P. Devi, “Sentiment Classification Using SVM and PSO,” pp. 1–3, 2016.

[4]      B. Pang, L. Lee, H. Rd, and S. Jose, “Thumbs up ? Sentiment Classification using Machine Learning Techniques,” 2002.

[5]      A. S. H. Basari, B. Hussin, I. G. P. Ananta, and J. Zeniarja, “Opinion Mining of Movie Review Using Hybrid Method of Support Vector Machine and Particle Swarm Optimization,” Procedia Eng., vol. 53, pp. 453–462, 2013.

[6]      E. Tuba, L. Mrkela, and M. Tuba, “Support Vector Machine Parameter Tuning using Firefly,” Conf. Radioelektronika, 2016.

[7]      A. Sharma, A. Zaidi, R. Singh, S. Jain, and A. Sahoo, “Optimization of SVM Classifier Using Firefly Algorithm,” pp. 198–202, 2013.

[8]      X. Yang, Cuckoo Search and Firefly Algorithm Theory and Applications. Springer, 2014.

[9]      A. Kowalczyk, “Support Vector Machines Succinctly,” Book, vol. Alexandre, 2017.

[10]    R. Diani, U. N. Wisesty, and A. Aditsania, “Analisis Pengaruh Kernel Support Vector Machine ( SVM ) pada Klasifikasi Data Microarray untuk Deteksi Kanker,” Ind. J. Comput., vol. 2, no. 1, NaN pp. 109–118, pp. 109–118, 2017.

[11]    F. Ratnawati, “Analisis Sentimen Opini Film Pada Twitter Menggunakan Algoritme Dynamic Convolutional Neural Network,” Universitas Gadjah Mada, 2017.

[12]    Suryadi, “Analisis Sentimen Review Hotel Menggunakan Algoritme Naive Bayes Classifier Dan Pendekatan Lexicon Based,” Universitas Gadjah Mada, 2017.

[13]    R. Wijayanti and A. Arisal, “Ensemble Approach for Sentiment Polarity Analysis in User-Generated Indonesian Text,” IEEE, pp. 158–163, 2017.

[14]    S. Patnaik and X. Li, Proceedings of International Conference on Computer Science and Information Technology (Advances in Intelligent Systems and Computing). 2013.

[15]    X. S. Yang, Z. Cui, R. Xiao, A. H. Gandomi, and M. Karamanoglu, Swarm Intelligence and Bio-isnpired Computation Theory and Applications. 2013.

[16]    B. Xing and Wen-Jing Gao, Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms. 2014.

DOI: https://doi.org/10.22146/ijccs.41302

Article Metrics

Abstract views : 233 | views : 198


  • There are currently no refbacks.

Copyright (c) 2019 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

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

Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs

View My Stats1
View My Stats2