Covid-19 Hoax Detection Using KNN in Jaccard Space

Utami, Ema; Iskandar, Ahmad Fikri; Hidayat, Wahyu; Prasetyo, Agung; Hartanto, Anggit Dwi

doi:10.22146/ijccs.67392

Cited by 2 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Untuk membangun model, kami menggunakan salah satu algoritma yang dapat melakukan klasifikasi data, yaitu algoritma Naive Bayes Classifier. Penelitian sebelumnya melakukan deteksi berita hoax Covid19 menggunakan algoritma K-Nearest Neighbor (KNN) [17], dan mendapatkan akurasi model akurasi sebesar 81%. Selain model yang digunakan pada penelitian sebelumnya berbeda, sumber data yang digunakan untuk deteksi hoax berita Covid19 pada penelitian sebelumnya juga berbeda.…”

Section: Hasil Dan Pembahasanunclassified

“…Dibandingkan dengan hasil penelitian sebelumnya yang juga melakukan deteksi berita hoax Covid19 menggunakan algoritma K-Nearest Neighbor (KNN) [17], algoritma Naïve Bayes Classifier yang diterapkan pada penelitian ini menghasilkan akurasi sebesar 81%. Hasilnya 5.1% lebih besar dibandingkan deteksi berita hoax menggunakan KNN pada penelitian sebelumnya yang mendapatkan akurasi sebesar 75.89%.…”

Section: Gambar 5 Akurasi Model Naïve Bayes Classifierunclassified

See 1 more Smart Citation

Implementasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Berita Palsu pada Sosial Media

Agustina,

Adrian,

Hermawati

2022

FaktorExacta

View full text Add to dashboard Cite

Hoax news (lie) on the internet has become a global problem that causes turmoil in society. Its presence can disrupt democratic order, the stability of social, cultural, political and economic life. The results of the research of the Indonesian Telematics Society showed that as many as 44.3% of respondents said they received fake news or misinformation every day. According to information released by Kominfo until August 11, 2021, there were 1848 hoax reports regarding the Covid-19 pandemic, 290 hoax reports regarding the Covid-19 Vaccine. Naïve Bayes Classifier is a classification method based on Bayes theorem, which in this paper is used to detect fake news on social media. The analysis was carried out using the Naïve Bayes Classifier algorithm, in this study using the CRISP-DM (Cross-Industry Standard Process for Data Mining) model. Training data sourced from the Kumparan site as much as 300 Data. In the process carried out using the python library for NLP, namely "<em>satrawi</em>". In testing the model using the confusion matrix method which consists of the number of rows of test data that are predicted to be true and false by the classification model used. At the deployment stage the model is pushed to Heroku so that users can predict news directly through the provided User Interface.

show abstract

Section: Hasil Dan Pembahasanunclassified

Section: Gambar 5 Akurasi Model Naïve Bayes Classifierunclassified

Implementasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Berita Palsu pada Sosial Media

Agustina,

Adrian,

Hermawati

2022

FaktorExacta

View full text Add to dashboard Cite

show abstract

“…Nevertheless, some of this information is false or part of a hoax. Several studies have focused on anticipating the emergence of false or hoax information on the internet, for instance in Arabia [1], Indonesia [2,3], India [21], and other countries. It has become a particular concern for researchers to prevent readers from being easily deceived by false information.…”

Section: Introductionmentioning

confidence: 99%

“…The K-Nearest Neighbour (KNN) method, based on the Jaccard Space, has previously been used to detect false information about COVID-19 in Indonesia [3]. The information was obtained from traditional media sources, Jabar Saber Hoaks and Jala Hoaks.…”

Section: Introductionmentioning

confidence: 99%

Detection Model for Fake News on Covid-19 in Indonesia

Rifai,

Mulyani,

Febrianto

et al. 2023

AEJ

View full text Add to dashboard Cite

Today, fake information has become a significant problem, exacerbated by the acceleration of access to information. The spread of fake information has a dangerous impact, especially regarding global health issues, for example COVID-19. People can access various resources to obtain information, including online sites and social media. One of the methods to control the spread of false information is detecting hoaxes. Many methods have been developed to identify hoaxes; most previous studies have focused on developing hoax detection methods using data from a single source in English. The present study is carried out to detect fake news in Indonesian language using multiple data sources, including traditional and social media in the context of COVID-19. The study uses Long Short-Term Memory (LSTM) and the Robustly Optimised Bidirectional Encoder Representations from Transformers Pre-Training Approach (RoBERTa). The LSTM approach is used to develop four different architectures that varied based on: (1) the use of text-only versus the use of both title and text; (2) the number of LSTM and dense layers; and (3) the activation function. The LSTM model with text-only data, a single LSTM layer and two dense layers, outperformed other LSTM architectures, achieving the highest accuracy of 92.17%. The LSTM models require a considerably short training time of 23–27 minutes for 3,847 articles and has a detection time of 3.8–4.1 ms per article. The RoBERTa classifiers outperformed all LSTM models with an accuracy of over 97% and a significantly better training time, with a margin of more than 50% compared to LSTM classifiers, although it had a slightly longer test time. Both LSTM and RoBERTa models outperformed the Naïve Bayes and SVM benchmark methods in terms of accuracy, precision, and recall. Therefore, this study shows that both LSTM and RoBERTa methods are reliable and can be reasonably implemented for real-time fake news detection.

show abstract

Covid-19 Hoax Detection Using KNN in Jaccard Space

Cited by 2 publications

References 16 publications

Implementasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Berita Palsu pada Sosial Media

Implementasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Berita Palsu pada Sosial Media

Detection Model for Fake News on Covid-19 in Indonesia

Contact Info

Product

Resources

About