Sentiment Analysis of Public Opinion Related to Rapid Test Using LDA Method

  • Viny Gilang Ramadhan Telkom University
  • Yuliant Sibaroni
Keywords: rapid test, twitter, LDA, sentiment, corona.


In 2020 the world will be shocked by an outbreak of a disease that has developed tremendously. This disease is the Coronavirus. The Indonesian government, in overcoming conducted a Rapid early detection test in the spread of the Coronavirus. The steps of the Indonesian government have received rejection in several areas because people consume hoax news on social media. Indonesians widely use Twitter in conversations about the Coronavirus. Previous research was carried out using large-scale data, which affected the performance of the topic extraction method. The classification used resulted in poor accuracy using LDA to find the probability of topics in existing documents. LDA excels in large-scale data processing and is more consistent in generating the topic proportion value and word probability. Aspect-based sentiment analysis on public opinion regarding the rapid test on Twitter using LDA can determine aspects and public opinion on the rapid test. The test results of this study obtained 7000 tweets, four aspects of the results of topic using LDA, and getting the best accuracy using the RBF kernel by 95%. The sentiment of the Indonesian people towards the Rapid test is positive, with 4,305 sentiments.


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J. Akbar, “1.254 Orang di Indonesia Meninggal Akibat Corona dalam 10 Hari, Ini Saran Epidemiolog",”, 2020. (accessed Oct. 14, 2020).

satuan tugas penanganan covid, “rapid test massal,” 2020. (accessed Oct. 15, 2020).

Replubika, “Menolak Rapid Test,” 2020. (accessed Nov. 27, 2020).

Y. Lin, “10 Twitter Statistics Every Marketer Should Know in 2019 [Infographic],” Oberlo Blog, 2019.

A. Fahmi, I. Ramadhan, P. Studi, S. Informasi, and F. I. Komputer, “Analisis Sentiment Masyarakat Selama Bulan Ramadhan Dalam Menghadapi Pandemi Covid-19,” vol. 1, no. 1, pp. 608–617, 2020.

N. S. Hari, “Analisis Sentimen Berbasis Aspek terhadap Ulasan Masyarakat pada Google Maps,” 2020.

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.

S. P. Astuti, “Analisis sentimen berbasis aspek pada aplikasi tokopedia menggunakan lda dan naïve bayes,” 2020.

M. Rezwanul, A. Ali, and A. Rahman, “Sentiment Analysis on Twitter Data using KNN and SVM,” Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 6, pp. 19–25, 2017, doi: 10.14569/ijacsa.2017.080603.

R. ; N. A. ; M. K. Risnantoyo, “JITE ( Journal of Informatics and Telecommunication Engineering ) Initial Centroid Optimization of K-Means Algorithm Using,” J. Informatics Telecommun. Eng., vol. 3, no. 2, pp. 224–231, 2020.

S. W. Kim and J. M. Gil, “Research paper classification systems based on TF-IDF and LDA schemes,” Human-centric Comput. Inf. Sci., vol. 9, no. 1, 2019, doi: 10.1186/s13673-019-0192-7.

S. Symeonidis, D. Effrosynidis, and A. Arampatzis, “A comparative evaluation of pre-processing techniques and their interactions for twitter sentiment analysis,” Expert Syst. Appl., vol. 110, pp. 298–310, 2018, doi: 10.1016/j.eswa.2018.06.022.

I. Mentaruk, A. Herdiani, and D. Puspandari, “Analisis Sentimen Twitter Transportasi Online Berbasis Ontologi ( Studi Kasus : Go-Jek ),” e-Proceeding Eng., vol. 6, no. 1, pp. 2029–2047, 2019.

R. KURNIAWAN and A. APRILIANI, “Analisis Sentimen Masyarakat Terhadap Virus Corona Berdasarkan Opini Dari Twitter Berbasis Web Scraper,” Jurnal INSTEK (Informatika Sains dan Teknologi), vol. 5, no. 1. p. 67, 2020, doi: 10.24252/instek.v5i1.13686.

I. M. K. B. Putra and R. P. Kusumawardani, “Analisis Topik Informasi Publik Media Sosial Di Surabaya Menggunakan Pemodelan Latent Dirichlet Allocation ( Lda ) Topic Analysis of Public Information in Social Media in Surabaya Based on Latent Dirichlet Allocation ( Lda ) Topic Modelling,” J. Tek. Its, vol. 6, no. 2, pp. 2–7, 2017.

M. Maryamah, A. Z. Arifin, R. Sarno, and R. W. Sholikah, “Enhanced topic modelling using dictionary for questions and answers problem,” Proc. 2019 Int. Conf. Inf. Commun. Technol. Syst. ICTS 2019, pp. 219–223, 2019, doi: 10.1109/ICTS.2019.8850986.

D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” J. Mach. Learn. Res., vol. 3, no. Jan, pp. 993–1022, 2003.

H. Jelodar et al., “Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey,” Multimed. Tools Appl., vol. 78, no. 11, pp. 15169–15211, 2019, doi: 10.1007/s11042-018-6894-4.

S. Qaiser and R. Ali, “Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents,” Int. J. Comput. Appl., vol. 181, no. 1, pp. 25–29, 2018, doi: 10.5120/ijca2018917395.

A. Rafiqi, “Penerapan Algoritma Fuzzy,” ADLN Univ. Airlangga, [Online]. Available: BAB II.pdf.

F. E. Cahyanti, Adiwijaya, and S. Al Faraby, “On the Feature Extraction for Sentiment Analysis of Movie Reviews Based on SVM,” 2020 8th Int. Conf. Inf. Commun. Technol. ICoICT 2020, 2020, doi: 10.1109/ICoICT49345.2020.9166397.

V. Amrizal, “Penerapan Metode Term Frequency Inverse Document Frequency (Tf-Idf) Dan Cosine Similarity Pada Sistem Temu Kembali Informasi Untuk Mengetahui Syarah Hadits Berbasis Web (Studi Kasus: Hadits Shahih Bukhari-Muslim),” J. Tek. Inform., vol. 11, no. 2, pp. 149–164, 2018, doi: 10.15408/jti.v11i2.8623.

D. Kim, D. Seo, S. Cho, and P. Kang, “Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec,” Inf. Sci. (Ny)., vol. 477, pp. 15–29, 2019, doi: 10.1016/j.ins.2018.10.006.

I. Mathilda Yulietha and S. Al Faraby, “Klasifikasi Sentimen Review Film Menggunakan Algoritma Support Vector Machine,” e-Proceeding Eng., vol. 4, no. 3, pp. 4740–4750, 2017.

S. J. Blair, Y. Bi, and M. D. Mulvenna, “Aggregated topic models for increasing social media topic coherence,” Appl. Intell., vol. 50, no. 1, pp. 138–156, 2020, doi: 10.1007/s10489-019-01438-z.

How to Cite
Viny Gilang Ramadhan, & Yuliant Sibaroni. (2021). Sentiment Analysis of Public Opinion Related to Rapid Test Using LDA Method. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(4), 672 - 679.
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