Identifikasi Kebutuhan Masyarakat Nusa Tenggara Barat pada Pandemi Covid-19 di Media Sosial dengan Metode Crawling

  • Jian Budiarto Universitas Bumigora Mataram
Keywords: Social Media Sentiment, covid-19 pandemic, NTB requirements

Abstract

People can write to the government about what they want through social media in the freedom of information era. Especially in the Covid-19 pandemic, the public's desire for government attention is getting higher. The purpose of this activity is to collect community needs at the beginning of the pandemic. The period of information retrieval is from March 30 to April 5, 2020. The activity uses the crawling method, which is gathering information with browser tools. The implementation stage starts from taking information with selenium, identifying posts and comments with Html tags, calculating the emergence of issues with N-gram NLP, and analyzing sentiment with Naive Bayes and Support-Vector Machine. The results showed that the community did not have panic to need foods with a total reaction of 172. The community was more worried about the spread of outbreaks from outside NTB with a reaction of 1,421.

Downloads

Download data is not yet available.

References

S. Tufféry, Data Mining and Statistics for Decision Making. John Wiley & Sons, 2011.

S. Raghavan dan H. Garcia-Molina, "Crawling the Hidden Web," 2000. http://ilpubs.stanford.edu:8090/456/ (diakses February 13, 2021).

G. Pant, P. Srinivasan, dan F. Menczer, "Crawling the Web," dalam Web Dynamics: Adapting to Change in Content, Size, Topology and Use, M. Levene dan A. Poulovassilis, Ed. Berlin, Heidelberg: Springer, 2004, hlm. 153–177.

M. Najork dan A. Heydon, "High-Performance Web Crawling," dalam Handbook of Massive Data Sets, J. Abello, P. M. Pardalos, dan M. G. C. Resende, Ed. Boston, MA: Springer US, 2002, hlm. 25–45.

D. Sartika, “Perbandingan Algoritma Klasifikasi Naive Bayes, Nearest Neighbour, dan Decision Tree pada Studi Kasus Pengambilan Keputusan Pemilihan Pola Pakaian | JATISI (Jurnal Teknik Informatika dan Sistem Informasi),” Diakses: Feb 13, 2021. [Daring]. Tersedia pada: http://jurnal.mdp.ac.id/index.php/jatisi/article/view/78.

D. Olson dan D. Delen, "Advanced Data Mining Techniques," 2008, doi: 10.5860/choice.45-6838.

I. Rish, "An Empirical Study of the Naïve Bayes Classifier," IJCAI 2001 Work Empir Methods Artif Intell, vol. 3, Jan 2001.

W. Feng, J. Sun, L. Zhang, C. Cao, dan Q. Yang, "A support vector machine based naive Bayes algorithm for spam filtering," dalam 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC), Des 2016, hlm. 1–8, doi: 10.1109/PCCC.2016.7820655.

B. T. Pham, D. Bui, I. Prakash, dan M. B. Dholakia, "Evaluation of predictive ability of support vector machines and naive Bayes trees methods for spatial prediction of landslides in Uttarakhand state (India) using GIS," vol. 10, no. 1, hlm. 10, 2016.

D. S. Vijayarani dan M. S. Dhayanand, "Liver Disease Prediction using SVM and Naïve Bayes Algorithms."

L. Dong, X. Li, dan G. Xie, "Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification," Abstract and Applied Analysis, 2014. https://www.hindawi.com/journals/aaa/2014/459137/ (diakses January 28, 2020).

G. A. Buntoro, “ANALISIS SENTIMEN HATESPEECH PADA TWITTER DENGAN METODE NAÏVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE,” J. Din. Inform., vol. 5, no. 2, Sep 2016, Diakses: Jan 28, 2020. [Daring]. Tersedia pada: http://ojs.upy.ac.id/ojs/index.php/dinf/article/view/975.

S. Hassan, M. Rafi, dan M. S. Shaikh, "Comparing SVM and naïve Bayes classifiers for text categorization with Wikitology as knowledge enrichment," dalam 2011 IEEE 14th International Multitopic Conference, Des 2011, hlm. 31–34, doi: 10.1109/INMIC.2011.6151495.

S. Rana dan A. Singh, "Comparative analysis of sentiment orientation using SVM and Naive Bayes techniques," dalam 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Okt 2016, hlm. 106–111, doi: 10.1109/NGCT.2016.7877399.

B. Y. Pratama dan R. Sarno, "Personality classification based on Twitter text using Naive Bayes, KNN and SVM," dalam 2015 International Conference on Data and Software Engineering (ICoDSE), Nov 2015, hlm. 170–174, doi: 10.1109/ICODSE.2015.7436992.

C. Troussas, M. Virvou, K. J. Espinosa, K. Llaguno, dan J. Caro, "Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning," dalam IISA 2013, Jul 2013, hlm. 1–6, doi: 10.1109/IISA.2013.6623713.

D. S. Jasim, "Data Mining Approach and Its Application to Dresses ...," moam.info. https://moam.info/data-mining-approach-and-its-application-to-dresses-_599343111723ddcb690daa67.html (diakses February 13, 2021).

B. Mirkin, "Data analysis, mathematical statistics, machine learning, data mining: Similarities and differences," dalam 2011 International Conference on Advanced Computer Science and Information Systems, Des 2011, hlm. 1–8.

“SeleniumHQ Browser Automation.” https://www.selenium.dev/ (diakses Feb 14, 2021).

Jan Kristanto Wibisono dan M. S. Drs. Edi Winarko, “OPINION MINING PADA TWITTER UNTUK BAHASA INDONESIA DENGAN METODE SUPPORT VECTOR MACHINE DAN METODE BERBASIS LEXICON,” Thesis, [Yogyakarta] : Universitas Gadjah Mada, 2013.

J. Kristanto, jankristanto/mythesis. 2017.

Published
2021-02-15
How to Cite
[1]
J. Budiarto, “Identifikasi Kebutuhan Masyarakat Nusa Tenggara Barat pada Pandemi Covid-19 di Media Sosial dengan Metode Crawling”, jtim, vol. 2, no. 4, pp. 244-250, Feb. 2021.
Section
Articles