Preprocessing of radicalism dataset to predict radical content in Indonesia

Subhan, Muh.; Sudarsono, Amang; Barakbah, Ali Ridho

doi:10.1109/kcic.2017.8228598

Cited by 2 publications

(3 citation statements)

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“…Recall is the percentage of correctly recognized positive labels. Here are the equations of f-measure, recall, precision, and accuracy [1]:…”

Section: Actual Class True Falsementioning

confidence: 99%

“…Radical content is more identical and often associated with ethnic and religious issues (SARA) [1]. According to the National Counterterrorism Agency, radicalization can encourage or provoke a group or individual to commit acts of violence in the name of religion [1]. The spread of radicals is often found on social media.…”

Section: Introductionmentioning

confidence: 99%

“…The methods used also vary, namely Naïve Bayes, Support Vector Machine, Logistic Regression, and KNN. In this research [1], the author proposes to select features that are categorized as radical or not radical using Human Brain and DF-Threshold. In this study, the results of the KNN method with the value of k=7 are the most stable compared to algorithms that achieve precision, recall, f-measure, and accuracy up to 66,37%.…”

Section: Introductionmentioning

confidence: 99%

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Radicalism Speech Detection in Indonesia on Twitter Using Backpropagation Neural Network Method

Musa

Sibaroni

2022

JoSYC

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In this modern era, many people use social media easily and freely. One of the social media used is Twitter. The reason people use Twitter is that they can express their opinion freely. However, this freedom does not always have a positive impact on other Twitter users. One of the negative impacts for users is that they can spread radical content. Therefore, this research aims to detect whether a tweet contains radical elements or not using the backpropagation neural network method. The process is carried out by taking data on Twitter, after which the preprocessing process is carried out. Then the data is processed using imbalanced handling, where the data is divided into oversampling and undersampling data. After the data is divided, the next process is to do stopword and then look for accuracy by comparing different epoch values, namely 100, 150, 200, and 250. The best epoch value obtained is 200, with a final accuracy result of 86%.

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“…Recall is the percentage of correctly recognized positive labels. Here are the equations of f-measure, recall, precision, and accuracy [1]:…”

Section: Actual Class True Falsementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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