Analisis Sentimen Netizen Terhadap Efisiensi APBN Menggunakan Orange Data Mining

The government's policy of APBN efficiency in 2025 has caused debate on social media including the X application (Twitter). Social media users and activists convey comments and opinions according to their respective sentiments, whether they agree, disagree, or are neutral towards government policies. This study will try to analyze sentiment on netizens' opinions and views using the Orange Data Mining application with the Sentiment Analysis - Multilingual Sentiment method. Testing is also carried out using the Naïve Bayes algorithm to measure the level of accuracy and prediction error. The results of the sentiment analysis in this study are that most support the APBN efficiency policy with a level of confidence and accuracy approaching 100%.

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Publication Details

Journal
El-Mujtama Jurnal Pengabdian Masyarakat
Published
2025-05-01
DOI
https://doi.org/10.47467/elmujtama.v5i3.7368
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
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article

Analisis Sentimen Netizen Terhadap Efisiensi APBN Menggunakan Orange Data Mining

Anto Febria, Joko Triloka
El-Mujtama Jurnal Pengabdian Masyarakat
Data Mining and Machine Learning Applications
article

Analisis Sentimen Netizen Terhadap Efisiensi APBN Menggunakan Orange Data Mining

Anto Febria, Joko Triloka
article en

Abstract

The government's policy of APBN efficiency in 2025 has caused debate on social media including the X application (Twitter). Social media users and activists convey comments and opinions according to their respective sentiments, whether they agree, disagree, or are neutral towards government policies. This study will try to analyze sentiment on netizens' opinions and views using the Orange Data Mining application with the Sentiment Analysis - Multilingual Sentiment method. Testing is also carried out using the Naïve Bayes algorithm to measure the level of accuracy and prediction error. The results of the sentiment analysis in this study are that most support the APBN efficiency policy with a level of confidence and accuracy approaching 100%.

El-Mujtama Jurnal Pengabdian MasyarakatVol. 5(3)
Informatics and Business Institute Darmajaya (ID)
Openalex Percentile: Top 88%
Data Mining and Machine Learning Applications
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