Analisis Sentimen Persepsi Masyarakat Terhadap Pemilu 2019 Pada Media Sosial Twitter Menggunakan Naive Bayes

 (*)Safitri Juanita Mail (Universitas Budi Luhur, Jakarta, Indonesia)

(*) Corresponding Author

Submitted: April 27, 2020; Published: July 20, 2020



According to the BAWASLU evaluation a variety of related negative content supports supporting prospective couples to burst into various social media pages. So sometimes the content leads to a hoax issue to the issue of religious and inter-group Racial (SARA). One of the social media used by the people of Indonesia is Twitter, according to number of Twitter daily users globally claimed to be increasing, this appears to be the 3rd Quarter Twitter Financial Report of 2019 on Twitter's 3rd quarter of 2019 Financial reports, daily active users on the Twitter platform are recorded to increase by 17 percent, to the number of 145 million users. So it is necessary that a sentiment analysis study can capture a pattern of community perception on social media Twitter against the 2019 elections and it is expected that this research can help interested parties to increase voter participation rate in the next 5 years. This research method uses the Indonesian tweet data taken from 16 April 2018-16 April 2019, further data in preprocessing, text transformation, stemming Bahasa Indonesia, specifying attribute class, load dictonary and a classification of Naive Bayes using Weka. The conclusion of this study was the classification of Naive Bayes finding that the 2019 election tweet dataset had a negative perception pattern of 52% much greater than the positive perception of 18% and the neutral perception had a value of 31% higher than positive perception. Naive Bayes ' degree of classification accuracy against the training dataset is 81% and the dataset testing 76%, the average precision value for positive sentiment is 86.65%, negative sentiment is 77.15%, and neutral sentiment is worth 80.95% while the average recall rate on positive sentiment is 36.8%, negative sentiment is 93.2% and the neutral sentiment is 86.8%


Community Perception, Elections 2019, Twitter, Naive Bayes, Weka

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S. Sorik, “Penataan Demokrasi dan Pemilu di Indonesia Pasca Reformasi,” J. Penelit. Polit., vol. 16, no. 1, pp. 101–107, 2019.

H. Abdulsalam, “Pemilu 2019 Dibayangi Ancaman Golput demi Liburan Baca,”, 2019. [Online]. Available: [Accessed: 04-Apr-2019].

E. Sulistyo, “Partisipasi Pemilih dalam Pemilu,” Koran Sindo, 05-Mar-2019.

A. Akbar et al., Serial Evaluasi Penyelenggaraan Pemilu Serentak 2019 Perihal Partisipasi Masyarakat. Jakarta: Badan Pengawas Pemilihan Umum (BAWASLU) RI, 2019.

B. Clinten, “Pengguna Aktif Harian Twitter Indonesia Di Klaim Terbanyak,”, 2019. [Online]. Available: [Accessed: 30-Dec-2019].

S. J. Pan, X. Ni, J. T. Sun, Q. Yang, and Z. Chen, “Cross-Domain Sentiment Classification via Spectral Feature Alignment,” in Proceedings of the 19th International Conference on World Wide Web, 2010, pp. 751–760.

H. Simorangkir and K. M. Lhaksmana, “Analisis Sentimen pada Twitter untuk Games Online Mobile Legends dan Arena of Valor dengan Metode Naïve Bayes Classifier,” e-proceeding of Englineering, vol. 5, no. 3, pp. 8131–8140, 2018.

G. A. Buntoro, “Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter,” Integer J., vol. 2, no. 1, pp. 32–41, 2017.

C. Prianto, N. H. Harani, and I. Firmansyah, “Analisis Sentimen Terhadap Kandidat Presiden Republik Indonesia Pada Pemilu 2019 di Media Sosial Twitter,” J. Media Inform. Budidarma, vol. 3, no. 4, pp. 405–413, 2019.

L. A. Andika, P. A. N. Azizah, and R. Respatiwulan, “Analisis Sentimen Masyarakat terhadap Hasil Quick Count Pemilihan Presiden Indonesia 2019 pada Media Sosial Twitter Menggunakan Metode Naive Bayes Classifier,” Indones. J. Appl. Stat., vol. 2, no. 1, p. 34, 2019.

P. Y. Saputra, “Implementasi Teknik Crawling Untuk Pengumpulan Data Dari Media Sosial Twitter,” J. Din. Dotcom, vol. 8, no. 2, pp. 160–168, 2017.

T. T. Sang Nguyen, “Model-Based Book Recommender Systems using Naïve Bayes enhanced with Optimal Feature Selection,” in ACM International Conference Proceeding Series, 2019, no. February, pp. 217–222.

J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. 2012.

I. H. Witten, E. Frank, and M. A. Hall, Data Mining Practical Machine Learning Tools and Techniques, 4th ed. United States: Morgan Kaufmann, 2016.

I. H. Witten, E. Frank, M. A. Hall, and C. J Pal, The WEKA Workbench online appendix. New Zealand: The University of Waikato, 2016.

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