Analysis of Social Media Users Sentiments Against Omnibus Law Based on Hashtags On Twitter

Okta Fanny, Heri Suroyo


From the research that has been done, it can be concluded that Sentiment Analysis can be used to know the sentiment of the public, especially Twitter netizens against omnibus law. After the sentiment analysis, it looks neutral artmen with the largest percentage of 55%, then positive sentiment by 35% and negative sentiment by 10%. The results of the analysis showed that the Naïve Bayes Classifier method provides classification test results with accuracy in Hashtag Pro with an average accuracy score of 92.1%, precision values with an average of 94.8% and recall values with an average of 90.7%. While Hashtag Counter For data classification, with an average accuracy value of 98.3%, precision value with an average of 97.6% and recall value with an average of 98.7%. The result of text cloud analysis conducted on a combination of hashtags both Hashtag pros and Hashtags cons, the dominant word appears is Omnibus Law which means that all hashtags in scrap is really discussing the main topic that is about Omnibus Law

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V. Chandani, “Komparasi Algoritma Klasifikasi Machine Learning Dan Feature Selection pada Analisis Sentimen Review Film,” J. Intell. Syst., vol. 1, no. 1, pp. 56–60, 2015.

N. Anggraini and H. Suroyo, “Comparison of Sentiment Analysis against Digital Payment ‘T-cash and Go-pay’ in Social Media Using Orange Data Mining,” J. Inf. Syst. Informatics, vol. 1, no. 2, pp. 152–163, 2019, doi: 10.33557/journalisi.v1i2.21.

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, doi: 10.13057/ijas.v2i1.29998.

Liu B, “Sentiment analysis and opinion mining,” Synth. Lect. Hum. Lang. Technol., vol. 5, pp. 1–167, 2012.

M. S. Utomo, “Web Scraping pada Situs Wikipedia menggunakan Metode Ekspresi Regular,” J. Teknol. Inf. Din., vol. 18, no. 2, pp. 153–160, 2013.

B. Zhao, “Encyclopedia of Big Data,” Encycl. Big Data, no. December, 2020, doi: 10.1007/978-3-319-32001-4.

D. Rustiana and N. Rahayu, “Analisis sentimen pasar otomotif mobil:,” J. SIMETRIS, vol. 8, no. 1, pp. 113–120, 2017.

T. Kwartler, “What is Text Mining?,” Text Min. Pract. with R, pp. 1–15, 2017, doi: 10.1002/9781119282105.ch1.

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, doi: 10.22146/ijccs.24716.

E. Vargiu and M. Urru, “Exploiting web scraping in a collaborative filtering- based approach to web advertising,” Artif. Intell. Res., vol. 2, no. 1, pp. 44–54, 2012, doi: 10.5430/air.v2n1p44.

E. H. Muktafin, K. Kusrini, and E. T. Luthfi, “Analisis Sentimen pada Ulasan Pembelian Produk di Marketplace Shopee Menggunakan Pendekatan Natural Language Processing,” J. Eksplora Inform., vol. 10, no. 1, pp. 32–42, 2020, doi: 10.30864/eksplora.v10i1.390.

S. Suryono, E. Utami, and E. T. Luthfi, “Klasifikasi Sentimen Pada Twitter Dengan Naive Bayes Classifier,” Angkasa J. Ilm. Bid. Teknol., vol. 10, no. 1, p. 89, 2018, doi: 10.28989/angkasa.v10i1.218.

R. Ferdiana, F. Jatmiko, D. D. Purwanti, A. S. T. Ayu, and W. F. Dicka, “Dataset Indonesia untuk Analisis Sentimen,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 8, no. 4, p. 334, 2019, doi: 10.22146/jnteti.v8i4.533.

F. T. Industri, “Studi Analisis Metode-Metode Parsing Dan Interpretasi Semantik Pada Natural Language Processing,” J. Inform., vol. 2, no. 1, pp. 13-22–22, 2001, doi: 10.9744/informatika.2.1.pp.13-22.

G. A. Buntoro, “Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter,” INTEGER J. Inf. Technol., vol. 1, no. 1, pp. 32–41, 2017, [Online]. Available:


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