Analisis Sentimen Perusahaan Listrik Negara Cabang Ambon Menggunakan Metode Support Vector Machine dan Naive Bayes Classifier

Hennie Tuhuteru; Ade Iriani

doi:10.30591/jpit.v3i3.977

Analisis Sentimen Perusahaan Listrik Negara Cabang Ambon Menggunakan Metode Support Vector Machine dan Naive Bayes Classifier

Hennie Tuhuteru, Ade Iriani

Abstract

Twitter is one of the most popular online networking service that accessed by the community / citizen. The account @ambonlima which has proven their credibility, take the opportunity to provide the information about the electricity in Ambon Island, Maluku. Power outage in Ambon is become the issue lately and the community conveyed via tweets addressed to @ambonlima accounts such as complaints, criticisms or supports. The opinion is textual data which can be extracted to know the sentiment of society to the performance of PT. PLN Ambon. The purpose of this research is to to found out the sentimental level of the society about the electrical condition in Ambon by using sentiment analysis method. There are two classification method used in this research, Naive Bayes Classifier (NBC) dan Support Vector Machine (SVM). In this case, the researcher will compare both method to understand which method have better accuracy. The NBC classification results using 2 fold in validation process showed a better accuracy than other fold value, which is 67.2%. Positive sentiment obtained 67%, neutral sentiment 19% and negative sentiment 14%. Meanwhile, the SVM classification method also showed a better accuracy using 2 fold. Positive sentiment derived 24%, neutral sentiment 29%, and negative sentiment 47%. This study shows the average level of accuracy of SVM classification method is better than the NBC method, which is 76.42%. The presence of negative sentiment that is not more than 50% indicates the influence of account @ambonlima which is able to afford the electrical problems to the public in real time.

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References

APJII, “Penetrasi & Perilaku Pengguna Internet Indonesia 2017,” Asos. Penyelenggara Jasa Internet Indones., pp. 1–39, 2017.

S. Kemp, “Digital in 2018 in Southeast Asia,” We Are Soc., p. 362, 2018.

PT Perusahaan Listrik Negara (Persero), Rencana Usaha Penyediaan Tenaga Listrik (RUPTL) PT PLN (Perserp) 2015 - 2024. Jakarta, 2015.

W. He, S. Zha, and L. Li, “Social media competitive analysis and text mining: A case study in the pizza industry,” Int. J. Inf. Manage., vol. 33, no. 3, pp. 464–472, 2013.

R. Feldman and J. Sanger, The Text Mining Handbook. New York: Cambridge University Press, 2007.

M. Kanakaraj and R. M. R. Guddeti, “Performance analysis of Ensemble methods on Twitter sentiment analysis using NLP techniques,” Proc. 2015 IEEE 9th Int. Conf. Semant. Comput. IEEE ICSC 2015, pp. 169–170, 2015.

P. K. Gajakosh, G. Tushar, and S. Rajashri, “Opinion Mining for Multi-Mix Languages Hotel Review by using Fuzzy Sets,” Int. Conf. Adv. Sci. Technol. 2015 (ICAST 2015) First, p. 4, 2015.

C. C. Aggarwal and C. X. Zhai, A Survey of Text Classification Algorithms. In: Aggarwal C., Zhai C. (eds) Mining Text Data. Boston, MA: Springer, 2012.

P. Tripathi, S. K. Vishwakarma, and A. Lala, “Sentiment Analysis of English Tweets Using RapidMiner,” 2015 Int. Conf. Comput. Intell. Commun. Networks, pp. 668–672, 2015.

S. K and D. R, “Designing a Machine Learning Based Software Risk Assessment Model Using Naïve Bayes Algorithm,” TAGA J. Graph. Technol., vol. 14, pp. 3141–3147, 2018.

N. Öztürk and S. Ayvaz, “Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis,” Telemat. Informatics, vol. 35, no. 1, pp. 136–147, 2018.

I. Zulfa and E. Winarko, “Sentimen Analisis Tweet Berbahasa Indonesia Dengan Deep Belief Network,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 11, no. 2, p. 187, 2017.

B. Liu, Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, 2012.

A. D’Andrea, F. Ferri, P. Grifoni, and T. Guzzo, “Approaches, Tools and Applications for Sentiment Analysis Implementation,” Int. J. Comput. Appl., vol. 125, no. 3, pp. 26–33, 2015.

S. B. Bhonde and J. R. Prasad, “Sentiment Analysis - Methods, Applications and Challenges,” Int. J. Electron. Commun. Comput. Eng., vol. 6, no. 6Online, pp. 2249–71, 2015.

M. Fernández-Gavilanes, T. Álvarez-López, J. Juncal-Martínez, E. Costa-Montenegro, and F. Javier González-Castaño, “Unsupervised method for sentiment analysis in online texts,” Expert Syst. Appl., vol. 58, pp. 57–75, 2016.

Z. H. Deng, K. H. Luo, and H. L. Yu, “A study of supervised term weighting scheme for sentiment analysis,” Expert Syst. Appl., vol. 41, no. 7, pp. 3506–3513, 2014.

M. Lan, C. L. Tan, J. Su, and Y. Lu, “Supervised and Traditional Term Weighting Methods for Automatic Text Categorization,” Pattern Anal. Mach. Intell. IEEE Trans., vol. 31, no. 4, pp. 721–735, 2009.

R. Moraes, J. F. Valiati, and W. P. Gavião Neto, “Document-level sentiment classification: An empirical comparison between SVM and ANN,” Expert Syst. Appl., vol. 40, no. 2, pp. 621–633, 2013.

P. K. Singh and M. Shahid Husain, “Methodological Study Of Opinion Mining And Sentiment Analysis Techniques,” Int. J. Soft Comput., vol. 5, no. 1, pp. 11–21, 2014.

E. Rish, “An empirical study of the naive Bayes classifier,” IJCAI-01 Work. Empir. Methods AI, no. January 2001, 2001.

N. Yunita, “ANALISIS SENTIMEN BERITA ARTIS DENGAN MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN PARTICLE SWARM OPTIMIZATION,” J. Sist. Inf. STMIK Antar Bangsa, vol. V, no. 2, pp. 104–112, 2016.

A. Amolik, N. Jivane, M. Bhandari, and M. Venkatesan, “Twitter sentiment analysis of movie reviews using machine learning technique,” Int. J. Eng. Technol., vol. 7, no. 6, pp. 2038–2044, 2016.

T. T. Wong, “Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation,” Pattern Recognit., vol. 48, no. 9, pp. 2839–2846, 2015.

R. GmbH, RapidMiner 8 Operator Reference Manual. RapidMiner GmbH, 2018.

DOI: https://doi.org/10.30591/jpit.v3i3.977