Levels of Political Participation Based on Naive Bayes Classifier


Rumaisah Hidayatillah(1*), Mirwan Mirwan(2), Mohammad Hakam(3), Aryo Nugroho(4)

(1) Department of Informatics Engineering, Universitas Narotama, Surabaya
(2) Department of Informatics Engineering, Universitas Narotama, Surabaya
(3) Department of Computer Systems, Universitas Narotama, Surabaya
(4) Faculty of Computer Science, Universitas Narotama, Surabaya, Indonesia
(*) Corresponding Author


Nowadays, social media is growing rapidly and globally until it finally became an important part of society. During campaign period for the regional head election in Indonesia, the candidates and their supporting parties actively use social media as a campaign tool. Social media like Twitter has been known as a political microblogging media that can provide data about current political event based on users’ tweets. By using Twitter as a data source, this study analyzes public participation during campaign period for 2018 Central Java regional head election. The purpose is to observe how much reaction is given to each candidate who advanced in the election. By using the crawling program, all tweets containing certain candidate names will be downloaded. After going through a series of preprocessing stages, data can be classified using Naive Bayes. Predictor features in classification datasets are the number of replies, retweets, and likes. While the target variable is reaction that is divided into three levels, including high, medium, and low. These levels are determined based on users’ reaction in a tweet. By using these rules, Naive Bayes managed to classify data correctly as much as 76.74% for Ganjar Pranowo and 68.81% for Sudirman Said.


social media; election campaign; naïve bayes

Full Text:



[1] A. G. Sooai, A. Nugroho, M. N. A. Azam, S. Sumpeno, and M. H. Purnomo, “Virtual artifact: Enhancing museum exhibit using 3D virtual reality,” in 2017 TRON Symposium (TRONSHOW), 2017.

[2] B. Joyce and J. Deng, “Sentiment analysis of tweets for the 2016 US presidential election,” in 2017 IEEE MIT Undergraduate Research Technology Conference (URTC), 2017.

[3] W. Hall, R. Tinati, and W. Jennings, “From Brexit to Trump: Social Media’s Role in Democracy,” Computer (Long. Beach. Calif)., vol. 51, no. 1, pp. 18–27, Jan. 2018.

[4] M. L. Khan, “Computers in Human Behavior Social media engagement : What motivates user participation and consumption on YouTube ?,” Comput. Human Behav., vol. 66, pp. 236–247, 2017.

[5] E. Colleoni, A. Rozza, and A. Arvidsson, “Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data,” J. Commun., vol. 64, no. 2, pp. 317–332, 2014.

[6] J. Groshek and K. Koc-Michalska, "Helping populism win? Social media use, filter bubbles, and support for populist presidential candidates in the 2016 US election campaign", Information, Communication & Society, vol. 20, no. 9, pp. 1389-1407, 2017.

[7] A. Nugroho, S. Sumpeno, M. H. Purnomo, “Visualizing Interaction in Catfiz Indonesian Messenger Using Graph Coloring,” NICOGRAPH International Conference, pp. 1234–1237, 2015.

[8] B. Álvarez-Bornstein, R. Costas, “Exploring the relationship between research funding and social media: disciplinary analysis of the distribution of funding,” STI 2018 Conference Proceedings, pp 1168–1181, 2018.

[9] A. Jungherr, "Twitter in Politics: A Comprehensive Literature Review," SSRN Electronic Journal, pp 1–90, 2014.

[10] M. Glowacki, V. Narayanan, S. Maynard, G. Hirsch, B. Kollanyi, L. Neudert, P. Howard, T. Lederer and V. Barash, "News and Political Information Consumption in Mexico: Mapping the 2018 Mexican Presidential Election on Twitter and Facebook", COMPROP DATA MEMO 2018.2/ JUNE 29, 2018.

[11] M. Sanguinetti, F. Poletto, C. Bosco, V. Patti, and M. Stranisci, “An Italian Twitter Corpus of Hate Speech against Immigrants,” in Proc. of the 11th International

Conference on Language Resources and Evaluation (LREC 2018), ELRA (2018), 2018

[12] S. Fano and D. Slanzi, "Using Twitter Data to Monitor Political Campaigns and Predict Election Results," The PAAMS Collection - 15th International Conference, 2017.

[13] K. W. Lim and W. Buntine, “Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon,” in Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 1319–1328, 2014.

[14] W. B. Zulfikar, M. Irfan, C. N. Alam, and M. Indra, “The comparation of text mining with Naive Bayes classifier, nearest neighbor, and decision tree to detect Indonesian swear words on Twitter,” 2017 5th Int. Conf. Cyber IT Serv. Manag. CITSM 2017, 2017.

[15] G. Boeing and P. Waddell, “New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings,” J. Plan. Educ. Res., vol. 37, no. 4, pp. 457–476, 2016.

[16] M. Sadegh, M. Shakeri Majd, J. Hernandez, and A. T. Haghighi, “The Quest for Hydrological Signatures: Effects of Data Transformation on Bayesian Inference of Watershed Models,” Water Resour. Manag., vol. 32, no. 5, pp. 1867–1881, 2018.

[17] Z. Wu, Q. Xu, J. Li, C. Fu, Q. Xuan, and Y. Xiang, “Passive Indoor Localization Based on CSI and Naive Bayes Classification,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 48, no. 9, pp. 1566–1577, 2018.

[18] M. N. M. Ibrahim and M. Z. M. Yusoff, “Twitter sentiment classification using Naive Bayes based on trainer perception,” in 2015 IEEE Conference on e-Learning, e-Management and e-Services (IC3e), pp. 187–189, 2015.

[19] G. R. Septianto, F. F. Mukti, M. Nasrun, and A. A. Gozali, “Jakarta congestion mapping and classification from twitter data extraction using tokenization and naïve bayes classifier,” Proc. - APMediaCast 2015 Asia Pacific Conf. Multimed. Broadcast., pp. 14–19, 2015.

[20] R. P. N. Budiarti, N. Widyatmoko, M. Hariadi, and M. H. Purnomo, “Web scraping for automated water quality monitoring system: A case study of PDAM Surabaya,” Proceeding - 2016 Int. Semin. Intell. Technol. Its Appl. ISITIA 2016 Recent Trends Intell. Comput. Technol. Sustain. Energy, pp. 641–648, 2017.

[21] O. Aborisade and M. Anwar, “Classification for Authorship of Tweets by Comparing Logistic Regression and Naive Bayes Classifiers,” in 2018 IEEE International Conference on Information Reuse and Integration (IRI), pp. 269–276, 2018.

DOI: https://doi.org/10.22146/ijccs.42531

Article Metrics

Abstract views : 4848 | views : 3298


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