Prediction of Indonesia Presidential Election Results for the 2019-2024 Period Using Twitter Sentiment Analysis

research
  • 23 Aug
  • 2020

Prediction of Indonesia Presidential Election Results for the 2019-2024 Period Using Twitter Sentiment Analysis

The main purpose of this research is to predict the election of the President and Vice President of the Republic of Indonesia from 2019-2024 through the mining process of public opinion on twitter and test it accurately with classification algorithms namely Support Vector Machine (SVM) with selection features of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). As the fourth largest democracy and the fifth largest twitter user in the world, twitter in Indonesia is very influential as a place for people to fight each other's arguments. Public talk about the election of two candidates for President and Vice President of the Republic of Indonesia for the 2019-2024 on Twitter, became an interesting topic for Twitter users with a variety of public sentiment both positive and negative. Therefore, there is a need for a method that helps to see public opinion effectively. The researchers used tweets in Indonesian as research data with keywords #jokowi2periode and #2019tetapjokowi, and #2019prabowosandi and #2019gantipresiden with a total of 4000 tweets. This study uses a classification technique, namely SVM Algorithm. This method is chosen based on many of the best classification techniques commonly used for the analysis of opinion sentiments. It is because of SVM has weaknesses for selecting the appropriate parameters or features. In this study, researchers made improvements to previous research using a feature selection comparison method, PSO & GA. The results of this study are in the form of a prediction of the pairs of candidates for the President and the Vice President of Indonesia for the period of 2019-2024 who have more positive sentiments. Based on public opinion on Twitter, the pair Prabowo Subianto-Sandiaga Uno is predicted to be elected as President and Vice President of Indonesia for the period of 2019-2024 with the most positive sentiment, reaching 830 out of 1000 tweets entered. And the SVM method of the combination of PSO is the best method with accuracy reaching 86.20% and the AUC value reaching 0.934.

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REFERENSI

[1] Sukirno, “Berebut posisi Wapres demi jadi Presiden,” Alinea.id. Jakarta, Apr-2018.
[2] G. Lazuardi, “KPU Tetapkan Pasangan Capres-Cawapres Pada 20 September 2018,” Tribunnews.com, Jakarta, 20-Sep-2018.
[3] A. Saubani, “KPU Tetapkan Dua Pasangan Calon Pilpres,” Republika.co.id. Jakarta, Sep-2018.
[4] G. Lazuardi, “Aktivitas Medsos Sebabkan Tensi Panas di Pemilu 2019,” www.tribunnews.com, May-2019.
[5] D. A. Kristiyanti, A. H. Umam, M. Wahyudi, R. Amin, and L. Marlinda, “Comparison of SVM & Naïve Bayes Algorithm for Sentiment Analysis Toward West Java Governor Candidate Period 2018-2023 Based on Public Opinion on Twitter,” Int. Conf. Cyber IT Serv. Manag. (CITSM 2018), no. Citsm, 2018.
[6] L. A. Abdillah, “Social Media As Political Party Campaign in Indonesia,” J. Ilm. MATRIK, vol. 16, no. 1, pp. 1–10, 2014.
[7] A. F. Hidayatullah and A. Sn, “Analisis Sentimen dan Klasifikasi Kategori Terhadap Tokoh Publik Pada Twitter,” Semin. Nas. Inform. 2014, vol. 2014, no. August 2013, pp. 0–8, 2014.
[8] J. H. Yang, “Indonesian Presidential Election: Will Social Media Forecasts Prove Right?,” RSIS Comment., no. 120, pp. 1–3, 2014.
[9] R. Dehkharghani, H. Mercan, A. Javeed, and Y. Saygin, “Sentimental causal rule discovery from Twitter,” Expert Syst. Appl., vol. 41, no. 10, pp. 4950–4958, Aug. 2014.
[10] A. Pak and P. Paroubek, “Twitter as a Corpus for Sentiment Analysis and Opinion Mining,” Ijarcce, vol. 5, no. 12, pp. 320–322, 2016.
[11] D. T. Lukmana, S. Subanti, and Y. Susanti, “Analisis Sentimen Terhadap Calon Presiden 2019 Dengan Support Vector Machine Di Twitter,” in Seminar Nasional Penelitian Pendidikan Matematika (SNP2M) 2019 UMT, 2019, pp. 154–160.
[12] A. Go, R. Bhayani, and L. Huang, “Twitter Sentiment Classification using Distant Supervision,” Twitter Sentim. Classif. using Distant Superv., pp. 1–12, 2009.
[13] A. Sarlan, C. Nadam, and S. Basri, “Twitter sentiment analysis,” Conf. Proc. - 6th Int. Conf. Inf. Technol. Multimed. UNITEN Cultiv. Creat. Enabling Technol. Through Internet Things, ICIMU 2014, no. November 2014, pp. 212–216, 2015.
[14] M. Wahyudi and D. A. Kristiyanti, “Sentiment Analysis of Smartphone Product Review Using Support Vector Machine Algorithm-Based Particle Swarm Optimization.,” J. Theor. Appl. Inf. Technol., vol. 91, no. 1, p. 189, 2016.
[15] Y. Ren, R. Wang, and D. Ji, “A Topic-Enhanced Word Embedding for Twitter Sentiment Classification,” Inf. Sci. (Ny)., 2016.
[16] R. S. Harahap, “KOMPARASI ALGORITMA KLASIFIKASI DECISION TREE, NAIVE BAYES DAN NEURAL NETWORK UNTUK PREDIKSI PENYAKIT GINJAL KRONIS,” Konf. Nas. Ilmu Pengetah. dan Teknol., vol. 2, no. 1, p. 239–INF.244, Aug. 2016.
[17] E. Haddi, X. Liu, and Y. Shi, “The Role of Text Pre-processing in Sentiment Analysis,” Procedia Comput. Sci., vol. 17, pp. 26–32, Jan. 2013.
[18] B. Pang and L. Lee, Opinion Mining and Sentiment Analysis, vol. 2, no. 1–2. Boston, USA: Foundations and Trends R ? in Information Retrieval, 2008.
[19] M. Annett and G. Kondrak, “A comparison of sentiment analysis techniques: Polarizing movie blogs,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5032 LNAI, no. Figure 1, pp. 25–35, 2008.
[20] M. Kaya, G. Fidan, and I. H. Toroslu, “Sentiment analysis of Turkish Political News,” in Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012, 2012, pp. 174–180.
[21] P. Sharma, “Prediction of Indian Election Using Sentiment Analysis on Hindi Twitter,” in 2016 IEEE International Conference on Big Data (Big Data) Prediction, 2016, pp. 1966–1971.
[22] M. Anjaria, R. Mohana, and R. Guddeti, “A novel sentiment analysis of social networks using supervised learning,” Soc. Netw. Anal. Min., 2014.
[23] S. Ceri, “Predicting Political Elections with Social Networks (The Case of Twitter in the 2012 U.S. Presidential Election),” Politecnico Di Milano, 2014.
[24] G. A. Buntoro, “Analisis Sentimen Calon Gubernur DKI Jakarta 41 2017 Di Twitter,” Integer J. Maret, vol. 1, no. 1, pp. 32–41, 2016.
[25] A. F. Hadi, D. B. C. W, M. Hasan, and A. D. Penelitian, “Text Mining Pada Media Sosial Twitter Studi Kasus : Masa Tenang Pilkada Dki 2017 Putaran 2,” 2017.
[26] G. A. Buntoro, T. B. Adji, and A. E. Purnamasari, “Sentiment Analysis Candidates of Indonesian Presiden 2014 with Five Class Attribute,” Int. J. Comput. Appl., vol. 136, no. 2, pp. 23–29, 2016.
[27] T. Elghazaly and A. Mahmoud, “Political Sentiment Analysis Using Twitter Data,” in ICC ’16 Proceedings of the International Conference on Internet of things and Cloud Computing, 2016.
[28] N. Monarizqa, L. E. Nugroho, and B. S. Hantono, “Penerapan Analisis Sentimen Pada Twitter Berbahasa Indonesia Sebagai Pemberi Rating,” J. Penelit. Tek. Elektro dan Teknol. Inf., vol. 1, pp.151–155, 2014.
[29] M. H. Rasyadi, “Analisis Sentimen Pada Twitter Menggunakan Metode Naïve Bayes (Studi Kasus Pemilihan Gubernur Dki Jakarta 2017),” Institut Pertanian Bogor, 2017.
[30] O. Almatrafi, S. Parack, and B. Chavan, “Application of LocationBased Sentiment Analysis Using Twitter for Identifying Trends Towards Indian General Elections 2014,” in IMCOM ’15, 2015.
[31] J. Smailovi, J. Kranjc, M. Grˇ, and M. Znidarˇ, “Monitoring the Twitter sentiment during the Bulgarian elections,” 2015.
[32] K. Charalampidou, “Estimating Popularity by Sentiment and Polarization Classification on Social Media,” Delft University of Technology, 2012.
[33] A. Hasan, S. Moin, A. Karim, and S. Shamshirband, “Machine Learning-Based Sentiment Analysis for Twitter Accounts,” Math. Comput. Appl., vol. 23, no. 11, pp. 1–15, 2018.
[34] W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Eng. J., May 2014.
[35] 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, Jan. 2013.
[36] M. Zhao, C. Fu, L. Ji, K. Tang, and M. Zhou, “Feature selection and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes,” Expert Syst. Appl., vol. 38, no. 5, pp. 5197–5204, May 2011.
[37] I. Habernal, “Sentiment Analysis in Czech Social Media Using Supervised Machine Learning,” no. June, pp. 65–74, 2013. [38] Alimuddin, M. Sadali, and M. Wasil, “Prediksi Hasil Pemilu Legislatif Kabupaten Lombok Timur Menggunakan Algoritma Naive Bayes Berbasis PSO,” J. Inform. Hamzanwadi, vol. 2, no.2527–6069, pp. 1–19, 2017.
[39] F. Nurhuda, S. W. Sihwi, and A. Doewas, “Analisis Sentimen Masyarakat terhadap Calon Presiden Indonesia 2014 berdasarkan Opini dari Twitter Menggunakan Metode Naive Bayes Classifier,”ITSmart J. Ilm. Teknol. dan Inf., vol. 2, no. 2, pp. 35–42, 2013.
[40] N. Chandran, “Indonesian President Jokowi Celebrates 2 Years in Office with an Eye on 2019 Vote,” in CNBC, 2016.
[41] Noname, “Jokowi-Ma’ruf Lawan Prabowo-Sandi,” sumutpos.co, Jakarta, 2018.
[42] B. Liu, Sentiment Analysis and Opinion Mining, no. April. Morgan & Claypool Publishers, 2012.
[43] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, “Lexicon-Based Methods for Sentiment Analysis,” Comput. Linguist., vol. 37, no. 2, pp. 267–307, 2011.
[44] J. Spencer and G. Uchyigit, “Sentimentor: Sentiment analysis of twitter data,” CEUR Workshop Proc., vol. 917, pp. 56–66, 2012.
[45] T. Carpenter and T. Way, “Tracking Sentiment Analysis through Twitter,” Proc. 2012 Int. Conf. Inf. Knowl. Eng. , no. Figure 1, 2013.
[46] R. Prabowo and M. Thelwall, “Sentiment Analysis: A Combined Approach,” J. Informetr., vol. 3, no. 2, pp. 143–157, 2009.
[47] C.-L. Huang and C.-J. Wang, “A GA-based feature selection and parameters optimizationfor support vector machines,” Expert Syst. Appl., vol. 31, no. 2, pp. 231–240, Aug. 2006.
[48] J.-S. Chou, M.-Y. Cheng, Y.-W. Wu, and A.-D. Pham, “Optimizing parameters of support vector machine using fast messy genetic algorithm for dispute classification,” Expert Syst. Appl., vol. 41, no. 8, pp. 3955–3964, Jun. 2014.
[49] Y. Liu, G. Wang, H. Chen, H. Dong, X. Zhu, and S. Wang, “An Improved Particle Swarm Optimization for Feature Selection,” J. Bionic Eng., vol. 8, no. 2, pp. 191–200, Jun. 2011.