Deteksi Hoax pada Berita Online Bahasa Inggris Menggunakan Bernoulli Naïve Bayes dengan Ekstraksi Fitur Tf-Idf

Prayoga, Agri Yodi; Hadiana, Asep Id; Umbara, Fajri Rakhmat

doi:10.46799/jsa.v2i10.327

Cited by 4 publications

(5 citation statements)

References 11 publications

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“…As a result, the accuracy of the two algorithms is below 68%, even the accuracy of the Naïve Bayes algorithm is only 66%. One effort to improve the performance of machine learning models to detect hoaxes is to use the TF-IDF feature extraction [6], [11]. In the research [11], the use of TF-IDF feature extraction in the Bernoulli Naive Bayes algorithm can increase accuracy by 16.08%, precision 15.7%, recall 16.22%, and f1-score 15.92% when compared to the results of previous studies.…”

Section: Introductionmentioning

confidence: 92%

“…One effort to improve the performance of machine learning models to detect hoaxes is to use the TF-IDF feature extraction [6], [11]. In the research [11], the use of TF-IDF feature extraction in the Bernoulli Naive Bayes algorithm can increase accuracy by 16.08%, precision 15.7%, recall 16.22%, and f1-score 15.92% when compared to the results of previous studies. Based on the short review above and literature [12], Naïve bayes algorithm is one of the most popular machine learning algorithms for detecting hoaxes.…”

Section: Introductionmentioning

confidence: 92%

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Hoax Detection on Indonesian Tweets using Naïve Bayes Classifier with TF-IDF

Karo Karo,

Romia,

Dewi

et al. 2023

josh

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Twitter is one of the most popular social media platforms in the world nowadays. Twitter users in Indonesia are the fifth largest in the world and are always active in expressing themselves and getting information through tweets. A hoax is a lie created as if it were true. Hoaxes are also often spread via tweets. The spread of hoaxes is extremely dangerous because it can cause social discord and even misunderstanding. Therefore, hoaxes must be resisted. This study aims to build a system to detect hoaxes on Indonesian tweets. The objective of this research is to identify hoax Indonesian tweets by using the Naïve Bayes classifier with Term Frequency Inverse Document Frequency (TF-IDF). This study collects and annotates tweets from hoax tweets post which sent by a user account. This study also applied several text preprocessing techniques to provide datasets. To provide the best hoax prediction model, this work splits datasets into training and testing datasets. There are four experimental scenarios that refer to splitting the dataset. The experimental results showed that the hoax prediction model using Naïve Bayes with TF-IDF had 64% accuracy and recall, 69% and 67% precision, and a F1-score respectively. This result is also superior to the hoax prediction model when using the Naïve Bayes classifier without the TF-IDF. It means that TF-IDF has made a positive contribution to improving model performance. Finally, this research contributes by detecting news with a proclivity for hoaxes and filtering what is classified as hoaxes or not.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 92%

Hoax Detection on Indonesian Tweets using Naïve Bayes Classifier with TF-IDF

Karo Karo,

Romia,

Dewi

et al. 2023

josh

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“…Kemudian pada [10] mengenai deteksi berita hoax dengan ekstraksi fitur TF-IDF menggunakan algoritma Bernoulli Naïve Bayes. Dataset terdiri dari 4 atribut yaitu title, text, subject, dan date dengan pelabelan menggunakan nilai biner 1 untuk fake news dan 0 untuk real news.…”

Section: Tinjauan Pustakaunclassified

Analisis Perbandingan Algoritma Naive Bayes Classifier dan Support Vector Machine untuk Klasifikasi Berita Hoax pada Berita Online Indonesia

Pratiwi

Sani

Winarno

et al. 2022

JMASIF

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Masyarakat mampu mengkonsumsi tiap informasi yang tersebar di internet dengan cepat dan terkadang informasi yang beredar tidak selalu memberikan kebenaran yang sesuai dengan kenyataannya (hoax). Demi mendapatkan keuntungan dan mencapai tujuan pribadi, hoax seringkali sengaja dibuat dan dibagikan. Informasi yang didapatkan dari hoax tentunya dapat mempengaruhi masyarakat karena menimbulkan keraguan dan kebingungan terhadap informasi yang diterima. Oleh karena itu, penelitian ini membahas tentang bagaimana mengklasifikasikan berita hoax berbahasa Indonesia mengenai isu kesehatan menggunakan TF-IDF serta algoritma Naïve Bayes Classifier dan SupportVector Machine dengan 4 model yang berbeda sehingga mampu memprediksi sebuah berita hoax atau valid. Pada penelitian ini dataset yang dikumpulkan sebanyak 287 diantaranya 200 valid dan 87 hoax. Hasil evaluasi model penelitian ini dengan menggunakan 4 model berbeda pada masing-masing algoritma, diperoleh nilai classification report terbesar untuk algoritma NBC pada model Complement Naïve Bayes dengan hasil precision 95.4%, recall 95.4%, f1-score 95.4% dan accuracy 93.1%. Sedangkan nilai classification report terbesar untuk algoritma SVM pada kernel Sigmoid dengan hasil precision 95.6%, recall 100%, f1-score 97.7% dan accuracy 96.5%. Sehingga dapat disimpulkan bahwa hasil performa rata-rata dari algoritma SVM memiliki kinerja yang lebih baik jika dibandingkan dengan algoritma NBC dalam melakukan klasifikasi berita hoax mengenai isu kesehatan.

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“…or is fake news so that it can provide an explanation to readers of the news (Granik & Mesyura, 2017). The detection of hoax news consistently increases from year to year, which means that efforts to stem the circulation of hoax news never end and continue to be implemented (Prayoga et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

“…Scientists explain that classifying a dataset is using a machine learning method which is an effective method for classifying data in the form of hoax news (Prayoga et al, 2021b). One of the machine learning algorithms is the C5.0 algorithm which is a development algorithm from the C4.5 algorithm where the process is almost similar, but the C5.0 algorithm has more value than the C4.5 algorithm.…”

Section: Introductionmentioning

confidence: 99%

Identification of Hoax News in the Using Community TF-RF and C5.0 Tree Decision Algorithm

Santoso,

Chrisnanto,

Abdillah

2023

Enrichment

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News has a great influence on social and political conditions, and news can drive the economy of a country. Identifying hoax news is very important to ensure that the information circulating in society is true and reliable, and helps limit the spread of false information. In the process of reading news spread on social media, people do not know whether it is fact or hoax news because they cannot distinguish whether the news circulating is real news or fake news which if left unchecked can result in the public being misinformed. Therefore, this research process is to create a sistem for identifying hoax news using Decision Tree C5.0, which is an algorithm for the development of the C4.5 algorithm which in a process is almost similar, but the C5.0 algorithm has more value than the C4.5 algorithm which is used for the data mining process with a classification method for 1000 data obtained by web scraping using the keywords "election 2024", "politics" and "checkfaktapilkadamafindo" on the Turnbackhoax.id and Detik.com sites. In this study, what distinguishes it from several previous studies is its existence in several test scenarios, namely classification using feature weighting, which in classification using feature weighting is TF.RF. After testing the confusion matrix on the C5.0 algorithm, it produces accuracy, precision, and recall on each training / test data (70/30) resulting in accuracy 79.33%, precision 80.00%, recall 97.00%, then training / test data (80/20) resulting in accruracy 79.50%, precision 81.00%, recall 95.00%, then training and test data (90/10) resulting in accuracy 72.00%, precision 74.00%, recall 89.00%.

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Deteksi Hoax pada Berita Online Bahasa Inggris Menggunakan Bernoulli Naïve Bayes dengan Ekstraksi Fitur Tf-Idf

Cited by 4 publications

References 11 publications

Hoax Detection on Indonesian Tweets using Naïve Bayes Classifier with TF-IDF

Hoax Detection on Indonesian Tweets using Naïve Bayes Classifier with TF-IDF

Analisis Perbandingan Algoritma Naive Bayes Classifier dan Support Vector Machine untuk Klasifikasi Berita Hoax pada Berita Online Indonesia

Identification of Hoax News in the Using Community TF-RF and C5.0 Tree Decision Algorithm

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