Evaluation of Feature Extraction TF-IDF in Indonesian Hoax News Classification

Prasetyo, Adi; Septianto, Bayu Dwi; Shidik, Guruh Fajar; Fanani, Ahmad Zainul

doi:10.1109/isemantic.2019.8884291

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…The result of the feature extraction process using TF-IDF is a vector that represents the text, and each word is assigned its respective weight [19]. The formula used to calculate the TF-IDF can be seen in the following formula [23]:…”

Section: E Feature Extractionmentioning

confidence: 99%

Implementation of Naïve Bayes Algorithm in Sentiment Analysis of Twitter Social Media Users Regarding Their Interest to Pay the Tax

Wahyu Andrian,

Adline Twince Tobing,

Zuhdi Pane

et al. 2023

International Journal of Science, Technology & Management

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Since 2008, tax revenue has failed to reach the target set in the State Budget each year. Until 2021, tax revenue managed to reach the target that had been targeted in the 2021 state budget. In the midst of improving tax revenue, towards the end of February 2023, a case involving the son of a Directorate General of Taxes (DGT) that made the father called by the Corruption Eradication Commission (CEC) to be asked for an explanation of his assets. After the case, there were many calls in the community to stop paying taxes, which was assessed by Tauhid Ahmad as Executive Director of Indef as a form of decreased trust in tax collecting institutions. This can affect the amount of revenue from taxes because trust in the government is one of the factors that tend to affect public compliance in paying taxes. Which can affect the amount of revenue from taxes because trust in the government is one of the factors that tend to affect public compliance in paying taxes. One of the crowded calls is the pros and cons of the tax boycott movement on Twitter. With the pros and cWith the pros and cons of the movement that can affect tax revenues on Twitter social media, an assessment based on sentiment analysis is needed which is divided into positive, neutral, or negative categories. Sentiment analysis in this research is carried out using three variations of Naïve Bayes assisted by the TF-IDF word weighting model, namely Gaussian Naïve Bayes, Multinomial Naïve Bayes, and Bernoulli Naïve Bayes. Then Confussion Matrix is used to evaluate the model by obtaining the accuracy, precission, recall, and f1-score values and the use of Synthetic Minority Oversampling Technique (SMOTE) to handle unbalanced data. The results of this study on unbalanced data, the implementation of Bernoulli Naïve Bayes using the SMOTE technique on a dataset comparison of 80:20 resulted in better performance than the variations of Gaussian and Multinomial Naïve Bayes with accuracy results of 91.03%, precision, 71.11%, recall 71.43%, and f1-score of 71.18%.

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Section: E Feature Extractionmentioning

confidence: 99%

Implementation of Naïve Bayes Algorithm in Sentiment Analysis of Twitter Social Media Users Regarding Their Interest to Pay the Tax

Wahyu Andrian,

Adline Twince Tobing,

Zuhdi Pane

et al. 2023

International Journal of Science, Technology & Management

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“…In this study, the feature extraction process was carried out using the n-gram method and the Term Frequency -Inverse Document Frequency (TF-IDF) method. An n-gram is a form of feature extraction that works by breaking sentences into a set of n-word combinations [3], [7].…”

Section: Feature Extractionmentioning

confidence: 99%

“…Calculations performed using Term Frequency -Inverse Document Frequency are carried out by combining Term Frequency calculations to determine the frequency of occurrence of words in a document, as well as performing Inverse Document Frequency calculations to determine the frequency of occurrence of words in documents so that it can be seen how important the word is in a document [7]. The general equation for calculating TF-IDF is as equation:…”

Section: Feature Extractionmentioning

confidence: 99%

“…𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 (𝑟𝑒𝑐𝑎𝑙𝑙) = 𝑇𝑃 𝑇𝑃 + 𝐹𝑁 (6) the F1-Score was used in this study using equation (7) to assess the proportion of the calculated precision and sensitivity values [19].…”

Section: 𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃 + 𝑇𝑁 𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁mentioning

confidence: 99%

“…The internet, with its various advantages, is now able to become the main axis as well as an important factor in the development of modern and online-based information media such as social media and publication media such as websites and blogs. Websites and Blogs are currently growing and have become one of the effective information publication media, especially those related to news channels [7]- [9]. In fact, with the development of Websites and Blogs as media for publication at this time, the existence of traditional information media such as radio and television, as well as print media in the form of magazines, newspapers, and tabloids are now gradually being abandoned [10], [11].…”

Section: Introductionmentioning

confidence: 99%

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Hoax news identification using machine learning model from online media in Bahasa Indonesia

Pratiwi¹

2022

MATRIX

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Information and communication technology that’s developing is one of the main triggers of the information explosion today. Nowadays, various news content is not only easy to obtain but also easy to produce through various platforms on the internet, including popular online media, such as blogs and websites. So a lot of news content on blogs and websites that are currently being circulated leads to fake news content (hoaxes) that can mislead the perception and thoughts of the readers. Therefore, it is important to develop a system that can detect the presence of fake news content to minimize the losses caused by the presence of fake news content. In this study, the Naive Bayes algorithm is proposed as a machine learning model that will be used to detect fake news content in Indonesian language online media. As a result, the global accuracy value reached 71% with recall, precision, and F1-Score values as a whole above 70% which indicates that the proposed model can detect fake news content quite well.

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Topic Classification Using Regularized Variable-Size CNN and Dynamic BPSO in Online Social Network

2023

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Evaluation of Feature Extraction TF-IDF in Indonesian Hoax News Classification

Cited by 9 publications

References 6 publications

Implementation of Naïve Bayes Algorithm in Sentiment Analysis of Twitter Social Media Users Regarding Their Interest to Pay the Tax

Implementation of Naïve Bayes Algorithm in Sentiment Analysis of Twitter Social Media Users Regarding Their Interest to Pay the Tax

Hoax news identification using machine learning model from online media in Bahasa Indonesia

Topic Classification Using Regularized Variable-Size CNN and Dynamic BPSO in Online Social Network

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