Analisis Sentiment Masyarakat terhadap Kasus Covid-19 pada Media Sosial Youtube dengan Metode Naive bayes

Muhammad Iqbal Ahmadi(1*), Dudih Gustian(2), Falentino Sembiring(3),

(1) Universitas Nusa Putra Sukabumi
(2) Universitas Nusa Putra Sukabumi
(3) Universitas Nusa Putra Sukabumi
(*) Corresponding Author

Abstract


The development of Covid-19 cases in Indonesia continues to increase. with the continued increase in these cases causing panic among the public regarding the presence or absence of this corona virus, in the midst of this condition an effective and efficient communication pattern is needed in providing education and information about this corona virus, for example with social media Youtube. Many people's responses to this news are expressed in the comments column. Therefore, a sentiment analysis model is needed to classify public comments into Positive, Negative and neutral. In this study, the Naive bayes method is used to build a sentiment analysis model for public responses about the news on the development of the Covid-19 case on the Youtube page, precisely on the KompasTV Chanel. Accuracy is 74% with the number of comments Positive 361, Negative 800 and neutral 490.

Full Text:

PDF

References


satgas Covid-19, “Analisis Data Covid-19 Indonesia Update Per 03 Januari 2021.” https://covid19.go.id/storage/app/media/Analisis Data COVID-19 Indonesia/2021/Januari/Analisis Data COVID-19 Mingguan Satuan Tugas PC19 per 03 Januari 2021 vFinal_compressed.pdf (accessed Jun. 08, 2021).

H. O. Y. Li, A. Bailey, D. Huynh, and J. Chan, “YouTube as a source of information on COVID-19: A pandemic of misinformation?,” BMJ Glob. Heal., vol. 5, no. 5, May 2020, doi: 10.1136/bmjgh-2020-002604.

M. Syarifuddin, “Analisis Sentimen Opini Publik Mengenai Covid-19 Pada Twitter Menggunakan Metode Naïve Bayes Dan Knn,” Inti Nusa Mandiri, vol. 15, no. 1, pp. 23–28, 2020.

Sharazita Dyah Anggita and Ikmah, “Algorithm Comparation of Naive bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 2, pp. 362–369, 2020, doi: 10.29207/resti.v4i2.1840.

D. G. Rita apriani, “Analisis Sentimen Dengan Naïve Bayes Terhadap Komentar Aplikasi Tokopedia,” vol. 6, no. 1, 2019.

A. L. Fairuz, R. D. Ramadhani, and N. A. Tanjung, “Analisis Sentimen Masyarakat Terhadap COVID-19 Pada Media Sosial,” J. DINDA, vol. 1, no. 1, pp. 10–12, 2021, [Online]. Available: http://journal.ittelkom-pwt.ac.id/index.php/dinda/article/view/180.

P. Y. Saputra, D. H. Subhi, and F. Z. A. Winatama, “Implementasi Sentimen Analisis Komentar Channel Video Pelayanan Pemerintah Di Youtube Menggunakan Algoritma Naïve Bayes,” J. Inform. Polinema, vol. 5, no. 4, pp. 209–213, 2019, doi: 10.33795/jip.v5i4.259.

W. Anggraini, M. Utami, J. Berlianty, and E. Sellya, “Klasifikasi Sentimen Masyarakat Terhadap Kebijakan Kartu Prakerja di Indonesia,” Fakt. Exacta, vol. 13, no. 4, pp. 255–261, 2021, doi: 10.30998/faktorexacta.v13i4.7964.

M. Christianto, J. Andjarwirawan, and A. Tjondrowiguno, “Aplikasi analisa sentimen pada komentar berbahasa Indonesia dalam objek video di website YouTube menggunakan metode Naïve Bayes classifier,” J. Infra, vol. 8.1, pp. 255–259, 2020.




DOI: http://dx.doi.org/10.30645/j-sakti.v5i2.378

Refbacks

  • There are currently no refbacks.


J-SAKTI (Jurnal Sains Komputer & Informatika)
Published Papers Indexed/Abstracted By:

Jumlah Kunjungan :

View My Stats