Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes

 Samsir Samsir (Universitas Al Washliyah, Rantauprapat, Indonesia)
 Ambiyar Ambiyar (Universitas Negeri Padang, Padang, Indonesia)
 Unung Verawardina (IKIP PGRI Pontianak, Pontianak, Indonesia)
 Firman Edi (Institut Teknologi Batam, Batam, Indonesia)
 (*)Ronal Watrianthos Mail (Universitas Al Washliyah, Rantauprapat, Indonesia)

(*) Corresponding Author

Submitted: November 16, 2020; Published: January 22, 2021

DOI: http://dx.doi.org/10.30865/mib.v5i1.2580

Abstract

The WHO announced that more than 52 million people tested positive for Covid-19, and 1.2 million died in the second week of November 2020. Meanwhile, Indonesia recorded 463 thousand individuals with 15,148 deaths that were confirmed positive. Strategy against pandemics by incorporating socialization. However, learning that was initially bold as a technique became controversial due to the briefness of the adaptation process. a wide continuum of social reactions has resulted in the sudden transition from face-to-face learning to bold learning on a large scale. This research focuses on public opinion on online learning during the Indonesian COVID-19 pandemic in early November 2020. The analysis was carried out on Twitter by mining document-based text that was interpreted using the Naïve Bayes algorithm. The results show that online learning has a positive sentiment of 30 percent, a negative sentiment of 69 percent, and a neutral 1 percent over the period. Due to community dissatisfaction about online learning, a significant amount of negative sentiment is created. Some tweets indicate disappointment with the words' stress 'and' lazy 'in the conversation being high-frequency words.

Keywords


COVID-19; Daring; Opinion; Sentiment; Naïve Bayes

Full Text:

PDF


Article Metrics

Abstract View: 259 times | PDF View: 156 times

References

C. Sohrabi et al., “World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19),” International Journal of Surgery. 2020, doi: 10.1016/j.ijsu.2020.02.034.

S. T. P. COVID-19, “Data Sebaran COVID-19 Indonesia,” covid19.go.id, 2020. https://covid19.go.id/ (accessed Nov. 14, 2020).

D. M. Dave, A. I. Friedson, K. Matsuzawa, J. J. Sabia, and S. Safford, “Black lives matter protests, social distancing, and COVID-19,” NBER Work. Pap. Ser., 2020.

A. Amindoni, “Virus corona: Presiden Jokowi pilih ‘pembatasan sosial dalam skala besar’, warga mulai sortir pendatang,” BBC News Indonesia, 2020. https://www.bbc.com/indonesia/indonesia-52059236 (accessed Oct. 07, 2020).

R. Watrianthos, “Analisis Pembelajaran Daring di Era Pandemic Covid-19,” in Merdeka Kreatif di Era Pandemi Covid-19: Suatu Pengantar, Medan: Green Press, 2020, p. 55.

R. H. Syah, “Dampak Covid-19 pada Pendidikan di Indonesia: Sekolah, Keterampilan, dan Proses Pembelajaran,” SALAM J. Sos. dan Budaya Syar-i, vol. 7, no. 5, Apr. 2020, doi: 10.15408/sjsbs.v7i5.15314.

Kumparan, “4 Kebijakan Nadiem Makarim soal Proses Belajar dari Rumah Selama Pandemi Corona,” kumparan.com, 2020. https://kumparan.com/kumparanmom/4-kebijakan-nadiem-makarim-soal-proses-belajar-dari-rumah-selama-pandemi-corona-1t5naOVW9MB/full (accessed Oct. 07, 2020).

N. H. Zhafira, Y. Ertika, and Chairiyaton, “PERSEPSI MAHASISWA TERHADAP PERKULIAHAN DARING SEBAGAI SARANA PEMBELAJARAN SELAMA MASA KARANTINA COVID-19,” J. Bisnis dan Kaji. Strateg. Manaj., vol. 4, no. 1, 2020.

S. Juanita, “Analisis Sentimen Persepsi Masyarakat Terhadap Pemilu 2019 Pada Media Sosial Twitter Menggunakan Naive Bayes,” J. MEDIA Inform. BUDIDARMA, vol. 4, no. 3, p. 552, Jul. 2020, doi: 10.30865/mib.v4i3.2140.

Y. Pratama, A. Roberto Tampubolon, L. Diantri Sianturi, R. Diana Manalu, and D. Frietz Pangaribuan, “Implementation of Sentiment Analysis on Twitter Using Naïve Bayes Algorithm to Know the People Responses to Debate of DKI Jakarta Governor Election,” in Journal of Physics: Conference Series, Mar. 2019, vol. 1175, p. 012102, doi: 10.1088/1742-6596/1175/1/012102.

D. A. Ramadhan and E. B. Setiawan, “ANALISIS SENTIMEN PROGRAM ACARA DI SCTV PADA TWITTER MENGGUNAKAN METODE NAIVE BAYES DAN SUPPORT VECTOR MACHINE,” in e-Proceeding of Engineering, 2019, pp. 9376–9743.

A. M. Zuhdi, E. Utami, and S. Raharjo, “ANALISIS SENTIMENT TWITTER TERHADAP CAPRES INDONESIA 2019 DENGAN METODE K-NN,” J. Inf. Politek. Indonusa Surakarta, vol. 5, no. 2, p. 7, 2019.

R. Ferryawan, Kusrini, and F. W. Wibowo, “ANALISIS SENTIMEN WISATA JAWA TENGAH MENGGUNAKAN NAΪVE BAYES,” J. Inf. Politek. Indonusa Surakarta, vol. 5, no. 3, pp. 55–60, 2019.

R. Tineges, A. Triayudi, and I. D. Sholihati, “Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM),” J. MEDIA Inform. BUDIDARMA, 2020, doi: 10.30865/mib.v4i3.2181.

C. B. Saputra, A. Muzakir, and D. Udariansyah, “ANALISIS SENTIMEN MASYARAKAT TERHADAP #2019GANTIPRESIDEN BERDASARKAN OPINI DARI TWITTER MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER,” in Bina Darma Conference on Computer Science, 2019, pp. 403--413.

B. Liu, Sentiment Analysis and Opinon Mining. California, USA: Morgan & Claypool Publishers, 2012.

S. B. Bhonde and J.R. Prasad, “Sentiment Analysis-Methods, Applications and Challenges,” nternational J. Electron. Commun. Comput. Eng., vol. 6, no. 6, pp. 634–640, 2015.

R. Watrianthos, S. Suryadi, D. Irmayani, M. Nasution, and E. F. S. Simanjorang, “Sentiment Analysis Of Traveloka App Using Naïve Bayes Classifier Method,” Int. J. Sci. Technol. Res., vol. 8, no. 07, pp. 786–788, 2019.

R. Rasenda, H. Lubis, and R. Ridwan, “Implementasi K-NN Dalam Analisa Sentimen Riba Pada Bunga Bank Berdasarkan Data Twitter,” J. MEDIA Inform. BUDIDARMA, 2020, doi: 10.30865/mib.v4i2.2051.

A. Sholihin, Haviluddin, N. Puspitasari, M. Wati, and Islamiyah, “Analisis Penyakit Difteri Berbasis Twitter Menggunakan Algoritma Naïve Bayes,” SAKTI – Sains, Apl. Komputasi dan Teknol. Inf., vol. 1, no. 1, pp. 7–15, 2019.

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, 2019, doi: 10.30865/mib.v3i4.1549.

G. E. I. Kambey, Rizal Sengkey, and Agustinus Jacobus, “Penerapan Clustering pada Aplikasi Pendeteksi Kemiripan Dokumen Teks Bahasa Indonesia,” J. Tek. Inform., vol. 15, no. 2, pp. 75–82, 2020.

X. Zhou, X. Tao, and Z. Yang, “Sentiment Analysis on Tweets for Social Events,” in IEEE 17th International Conference on Computer Supported Coorporative Work in Design, 2013, pp. 557–562.

X. Gao, R. Tan, and G. Li, “Research on Text Mining of Material Science Based on Natural Language Processing,” IOP Conf. Ser. Mater. Sci. Eng., vol. 768, p. 072094, Mar. 2020, doi: 10.1088/1757-899X/768/7/072094.

J. M and V. H, “Opinion Mining For Sentiment Data Classification,” Int. J. Res. Inf. Technol., vol. 3, no. 1, pp. 1–13, 2014.

R. P and M. M, “Sentiment Analysis of User Generated Twitter Updates using Various Classification,” 2009.

N. Rochmawati and S. C. Wibawa, “Opinion Analysis on Rohingya using Twitter Data,” IOP Conf. Ser. Mater. Sci. Eng., vol. 336, no. 1, 2018, doi: 10.1088/1757-899X/336/1/012013.

Z. Dong, X. Guo, S. Rajana, and B. Chen, “Understanding 21st century bordeaux wines from wine reviews using naïve bayes classifier,” Beverages, 2020, doi: 10.3390/beverages6010005.

D. Setian and I. Seprina, “ANALISIS SENTIMEN MASYARAKAT TERHADAP DATA TWEET LAZADA MENGGUNAKAN TEXT MINING DAN ALGORITMA NAIVE BAYES CLASSIFIER,” in Bina Darma Conference on Computer Science, 2020, pp. 998–1004.

I. Fahmi, “Drone Emprit Academic: Software for social media monitoring and analytics,” dea, 2020. academic.droneemprit.id (accessed Oct. 31, 2020).

D. R. Lazuardi, T. A. Munandar, H. Harsiti, Z. Mutaqin, and R. N. Hays, “Sentiment analysis of public opinions on the welfare of honorary educators using Naive Bayes,” IOP Conf. Ser. Mater. Sci. Eng., vol. 830, p. 032018, May 2020, doi: 10.1088/1757-899X/830/3/032018.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 JURNAL MEDIA INFORMATIKA BUDIDARMA

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



JURNAL MEDIA INFORMATIKA BUDIDARMA
STMIK Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 Telp 061-7875998
email : mib.stmikbd@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.