Rancang Bangun Model Name Entity Recognition Menggunakan Metode Backpropagation dalam Klasifikasi Berita Hoaks Seputar Vaksin Covid-19
Sari
The flow of news regarding the development of Covid-19 has dominated various information channels in Indonesia in the last 2 years, either through print or digital media. Various types of news related to Covid-19 continue to circulate, including hoax news. One of the most widely circulated hoax news is the news about the Covid-19 vaccine. The rise of information containing hoax news and untrue rumors about the Covid-19 vaccine in the community can worsen the pandemic situation. Currently, there is no intelligent system capable of classifying hoaxes about the Covid-19 vaccine. To maximize the prevention of the spread of hoax news about the Covid-19 vaccine and overcome the problems faced, the researchers designed a classification system for hoax news about the Covid-19 vaccine with a machine learning approach. The system built can classify news with a combination of the Backpropagation Name Entity Recognition (NER) algorithm. Dataset used is 600 Covid-19 vaccine news data obtained from the sites https://turnbackhoax.id/ and https://www.kompas.com/ with the keyword "vaksin covid". Dataset divided into two, training data and test data. The training data is preprocessed and then used in model design. Test data is used to evaluate the results of model design. This process produces a machine learning model with accuracy rate of 97,62%. From these results, the system is able to classify news texts about Covid-19 vaccine. From these results, the system is able to classify news texts about Covid-19 vaccine.
Keywords — Backpropagation, Hoax news, NER
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D. H. Jayani, ―Kemenkominfo Turunkan 1.870 Konten Hoaks Vaksin
Covid-19 di Media Sosial | Databoks.‖
https://databoks.katadata.co.id/datapublish/2021/07/21/kemenkominfoturunkan-1870-konten-hoaks-vaksin-covid-19-di-media-sosial
(accessed Dec. 05, 2021).
C. Juditha, ―Interaksi Komunikasi Hoax di Media Sosial serta
Antisipasinya Hoax Communication Interactivity in Social Media and
Anticipation,‖ 2018.
M. Aziz Assuja, ―ANALISIS SENTIMEN TWEET
MENGGUNAKAN BACKPROPAGATION NEURAL NETWORK,‖
Jurnal TEKNOINFO, vol. 10, no. 2, pp. 1693–1703, 2016.
] A. Tabitha, B. Panjaitan, and D. I. Santoso, ―Deteksi Hoaks Pada
Berita Berbahasa Indonesia Seputar COVID-19,‖ Jurnal Format, vol.
, no. 1, pp. 76–85, 2021.
S. Kim, S. Choi, and J. Seok, ―Keyword Extraction in Economics
Literatures using Natural Language Processing,‖ International
Conference on Ubiquitous and Future Networks, ICUFN, vol. 2021-
August, pp. 75–77, Aug. 2021, doi:
1109/ICUFN49451.2021.9528546.
C. Wintang Kencana, E. B. Setiawan, and I. Kurniawan, ―Hoax
Detection on Twitter using Feed-forward and Back-propagation Neural
Networks Method,‖ masa berlaku mulai, vol. 1, no. 3, pp. 648–654,
I. Budiman, R. Faisal, and D. T. Nugrahadi, ―Studi Ekstraksi Fitur
Berbasis Vektor Word2Vec pada Pembentukan Fitur Berdimensi
Rendah,‖ Jurnal Komputasi, vol. 8, no. 1, pp. 62–69, Apr. 2020,
D. Wang, J. Su, and H. Yu, ―Feature extraction and analysis of natural
language processing for deep learning english language,‖ IEEE Access,
vol. 8, pp. 46335–46345, 2020, doi: 10.1109/ACCESS.2020.2974101.
H. Liang, X. Sun, Y. Sun, and Y. Gao, ―Text feature extraction based
on deep learning: a review,‖ EURASIP J Wirel Commun Netw, vol.
, no. 1, Dec. 2017, doi: 10.1186/S13638-017-0993-1.
I. Roldos, ―Introduction to Entity Extraction: What Is It And How It
Works,‖ Nov. 09, 2020. https://monkeylearn.com/blog/entityextraction/ (accessed Dec. 06, 2021).
S. Kannan et al., ―Big Data Analytics for Social Media,‖ Big Data:
Principles and Paradigms, pp. 63–94, Jun. 2016, doi: 10.1016/B978-0-
-805394-2.00003-9.
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