Detecting Hoaxes in Indonesian News Using TF/TDM and K Nearest Neighbor

Zuliarso, Eri; Anwar, Muchamad Taufiq; Hadiono, Kristophorus; Chasanah, Iswatun

doi:10.1088/1757-899x/835/1/012036

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…The purpose of the stop word removal process is to speed up process performance because it reduces some common words, as well as to improve model performance because the data to be modeled is data that specifically contains important words that represent a certain class [4]- [6], [11]. For the stop word removal process carried out in this study, the researcher used the Indonesian language stop word list from Talla F.Z [17].…”

Section: Figure 2 Pre-processing Stepsmentioning

confidence: 99%

“…As a result, information is now easier, faster, and more flexible to be accessed anywhere and anytime regardless of the location and place where the information is located. These developments certainly cannot be separated from the role of the internet which is currently evolving continuously and has even transformed into a major need for humans [4]- [6].…”

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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Section: Figure 2 Pre-processing Stepsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Pratiwi¹

2022

MATRIX

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“…We are using Term Frequency-Inverse Document Frequency (TF-IDF) to give weights for every word to create a vector space for the text. TF-IDF is commonly used in text classification tasks such as in [1]. This research is essentially a multiclass classification problem.…”

Section: -03mentioning

confidence: 99%

Automatic Complaints Categorization Using Random Forest and Gradient Boosting

Anwar

Pratiwi

Udhayana

2021

ASSET

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Capturing and responding to complaints from the public is an important effort to develop a good city/country. This project aims to utilize Data Mining to automatize complaints categorization. More than 35,000 complaints in Bangalore city, India, were retrieved from the “I Change My City” website (https://www.ichangemycity.com). The vector space of the complaints was created using Term Frequency–Inverse Document Frequency (TF-IDF) and the multi-class text classifications were done using Random Forest (RF) and Gradient Boosting (GB). Results showed that both RF and GB have similar performance with an accuracy of 73% on the 10-classes multi-class classification task. Result also showed that the model is highly dependent on the word usage in the complaint's description. Future research directions to increase task performance are also suggested.

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“…This classification problem can be solved automatically using Deep Learning models. There have been many studies on the classification of hoaxes in Indonesian, from those using word similarity measurement theories such as Text Rank and Dice Similarity [4], Extreme Gradient Boosting [5], classic machine learning methods such as Naive Bayes [6]- [9], K-Nearest Neighbor [10], Random Forest [7], Decision Tree [7], and Support Vector Machine [6], [7], [11], to deep learning methods such as Convolution Neural Network (CNN) [6], [12], Recurrent Neural Network (RNN) [13], Long Short-Term Memory (LSTM) [6], [14], and Gated Recurrent Unit (GRU) [6], [12], [14]. The deep learning methods that have been widely used to classify hoaxes in Indonesian language each have advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Hoax Classification in Indonesian Language with Bidirectional Temporal Convolutional Network Architecture

Maulana

Asih

2023

J. Soft Comput. Explor.

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The increasingly massive rate of information dissemination in cyberspace has had several negative impacts, one of which is the increased vulnerability to the spread of hoaxes. Hoax has seven classifications. Classification problems such as hoax classification can be automated using the application of the Deep Learning model. Bidirectional Temporal Convolutional Network (Bi-TCN) is a type of Deep Learning architectural model that is very suitable for text classification cases. Because the architecture uses dilation factors in its feature extraction so it can generate exceptionally large receptive fields and is supported by Bidirectional aggregation to ensure that the model can learn long-term dependencies without storing duplicate context information. The purpose of this study is to evaluate the performance of Bi-TCN architecture combined with pre-trained FastText embedding model for hoax classification in Indonesian and implement the resulting model on website. Based on the research that has been done, the model with Bi-TCN architecture has satisfactory performance with an accuracy score of 92.98% and a loss value that can be reduced to 0.191. Out of a total of 13,673 data tested with this model, only 414 data or in other words around 3% of the total data were incorrect predictions.

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Detecting Hoaxes in Indonesian News Using TF/TDM and K Nearest Neighbor

Cited by 11 publications

References 11 publications

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

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

Automatic Complaints Categorization Using Random Forest and Gradient Boosting

Hoax Classification in Indonesian Language with Bidirectional Temporal Convolutional Network Architecture

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