Analisis Sentimen Ciutan Twitter Terkait Penerapan Permendikbudristek Nomor 30 Tahun 2021 Menggunakan TextBlob dan Support Vector Machine

Afrillia, Yesy; Rosnita, Lidya; Siska, Deassy

doi:10.33379/gtech.v6i2.1778

Cited by 4 publications

(5 citation statements)

References 1 publication

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“…This can also help in the ratification of the PKS Bill because, at this time, the community, especially women, have started to have the courage to voice acts of violence experienced on social media, especially on the Komnas Perempuan Twitter account, which is the platform. This analysis shows that the biggest narrative results in the top 10 narratives are dominated by the use of Hashtags [42]. This points to the use of hashtags as a medium that is easy to do in an effort to disseminate information related to the protection of women's rights and obligations [43].…”

Section: Narrative Analysis Of Komnas Perempuan's Twitter Social Medi...mentioning

confidence: 93%

Social Media Analysis On The Urgency Of Passing Bill Elimination Violence against Women (RUUPKS) 2022

Angesti,

Fatikha,

Ramadan

et al. 2023

E3S Web Conf.

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Violence cases are a form of criminal activity regardless of age or gender. This study aims to analyze the role of Komnas Perempuan on Twitter in the urgency of ratifying the Bill on the Elimination of Violence against Women (RUUPKS) in 2022. This study uses a qualitative approach that aims to examine certain social phenomena. In addition, this research uses Qualitative Data Analysis Software (Q-DAS). Then the data were analyzed using Nvivo 12Plus software. The results obtained: First, in 2021, the KomnasPerempuan Twitter account is considered the most active in providing information about violence against women. Second, the KomnasPerempuan Twitter account produces content that demonstrates the authenticity of content. Third, the developing narrative on Twitter raises cases of sexual violence using hashtags, especially women and children. Fourth, KomnasPerempuan's Twitter content refers to four topics that are often discussed, namely information media with a percentage of 41%, participation media at 28%, narrative media at 18% and coordination media with a percentage of 13%. This research is used to complement previous research that focused on the Twitter social media activism phenomenon related to RUUPKS in the scope of human rights.

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Section: Narrative Analysis Of Komnas Perempuan's Twitter Social Medi...mentioning

confidence: 93%

Social Media Analysis On The Urgency Of Passing Bill Elimination Violence against Women (RUUPKS) 2022

Angesti,

Fatikha,

Ramadan

et al. 2023

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…Confusion Matrix A confusion matrix is a process used in analyzing the accuracy of classification models made to identify data from different classes. By measuring the level of accuracy, the performance of a classification model can be known (Afrillia et al, 2022). In addition, to make the data easier to understand by readers, at this stage, data visualization is carried out using word clouds to display words based on their frequency of occurrence, both in uploads with positive and negative sentiments.…”

Section: Evaluation Dan Visualizationmentioning

confidence: 99%

Sentiment Analysis of Mobile Provider Application Reviews Using Naive Bayes Algorithm and Support Vector Machine

Ningsih,

Hermanto,

Nugroho

2024

SinkrOn

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To choose a mobile provider to use, prospective users often rely on reviews left by previous users of the mobile provider application. One source of information for finding reviews of cellular provider applications is the Google Play Store. The purpose of this research is to analyze user reviews of cellular provider applications and find out the comparison of the accuracy levels of the two algorithms to be used, namely the Naïve Bayes Classification (NBC) and Support Vector Machine (SVM) algorithms. The object of this research is focused on the three most popular applications in Indonesia, according to the Goodstate website, namely Telkomsel, IM3, and XL Axiata. After testing using the Naïve Bayes Clasification method, the accuracy value obtained in the MyTelkomsel application is 75%, MyIM3 is 80%, and MyXL is 72%. While the Support Vector Machine method obtained an accuracy value of 77% for MyTelkomsel, 80% for MyIM3, and 76% for MyXL.

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“…Case Folding is a procedure used to equate the shape of letters in data into a lowercase form [11]. The output of the Case Folding process is described in table 1 dalam hal sanksi administratif yang diberikan kepada pse lingkup privat sebagaimana dimaksud pada ayat (1) adalah pemutusan akses terhadap sistem elektronik (access blocking), menteri melakukan normalisasi berdasarkan pengajuan rekomendasi oleh kementerian atau lembaga atas dasar layanan pse lingkup privat yang telah memenuhi ketentuan peraturan perundangundangan.…”

Section: Case Foldingunclassified

Sentiment Analysis of Content Permenkominfo NO.5 of 2020 Using a Classification Algorithm

Amada

Munawar

Pilliang

2023

jitk

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This study aims to evaluate the impact of the policy issued by the Minister of Communication and Information Technology (PERMENKOMINFO No.5 of 2020) on the public's ability to access content through Private Scope Electronic System Providers (PSE). The study uses sentiment analysis and data classification methods to analyze the content of PERMENKOMINFO No.5 of 2020 and provides results on the accuracy of sentiment prediction. The results of the study show that the data classification method in sentiment analysis can provide accurate results in predicting the sentiment towards the content of PERMENKOMINFO No.5 of 2020. The study also highlights the need for improvement and better policy to ensure the interests of the public in accessing online information. The negative sentiment of 80.34% obtained through sentiment analysis provides important contributions for policy evaluation and feedback for improvement. This study provides valuable insights into the public's sentiment towards the PERMENKOMINFO No.5 of 2020 policy and its impact on their ability to access content. It also contributes to understanding the legal uncertainty in accessing content and reinforces the case for better policy to ensure the interests of the public.

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Analisis Sentimen Ciutan Twitter Terkait Penerapan Permendikbudristek Nomor 30 Tahun 2021 Menggunakan TextBlob dan Support Vector Machine

Cited by 4 publications

References 1 publication

Social Media Analysis On The Urgency Of Passing Bill Elimination Violence against Women (RUUPKS) 2022

Social Media Analysis On The Urgency Of Passing Bill Elimination Violence against Women (RUUPKS) 2022

Sentiment Analysis of Mobile Provider Application Reviews Using Naive Bayes Algorithm and Support Vector Machine

Sentiment Analysis of Content Permenkominfo NO.5 of 2020 Using a Classification Algorithm

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