Analisis Sentimen Terhadap Calon Presiden Indonesia 2024 dengan Metode Extreme Gradient Boosting (XGBOOST)

In 2024, Indonesia will implement democracy in the election of the Indonesian head of state. Any political figure who runs for head of state and calculates his popularity based on public opinion. After the General Election Commission (KPU) released the names of the 2024 Indonesian presidential candidates, these names were widely discussed, especially on social networks, one of which was Twitter. Twitter or what is often called X is a platform that provides short, concise and clear information. Twitter users responding to the 2024 presidential candidate have different opinions on Twitter. The sentiments used are positive, negative and neutral. The method used to analyze public opinion with data processed on Twitter social media uses Extreme Gradient Boosting (XGBOOST), classifying tweet test data in the form of classification with prediction output with accurate values. This research takes Twitter data to see public opinion on presidential candidates. The aim of this research is to determine the process of digital text analysis and the application of the XGBOOST method to Twitter user sentiment in two categories (positive and negative) and three categories (positive, negative and neutral) for each candidate, namely Ganjar Pranowo, Anies Baswedan and Prabowo Subianto. The results show an accuracy of 0.96%, precision of 0.96% and recall of 0.97%.

Authors

Institutions

Publication Details

Journal
Jurnal Informatika Jurnal Pengembangan IT
Published
2024-12-17
DOI
https://doi.org/10.30591/jpit.v9i3.6127
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analisis Sentimen Terhadap Calon Presiden Indonesia 2024 dengan Metode Extreme Gradient Boosting (XGBOOST)

Yulistiani Yulistiani, Styawati Styawati
Jurnal Informatika Jurnal Pengembangan IT
Data Mining and Machine Learning Applications
article

Analisis Sentimen Terhadap Calon Presiden Indonesia 2024 dengan Metode Extreme Gradient Boosting (XGBOOST)

Yulistiani Yulistiani, Styawati Styawati
article en

Abstract

In 2024, Indonesia will implement democracy in the election of the Indonesian head of state. Any political figure who runs for head of state and calculates his popularity based on public opinion. After the General Election Commission (KPU) released the names of the 2024 Indonesian presidential candidates, these names were widely discussed, especially on social networks, one of which was Twitter. Twitter or what is often called X is a platform that provides short, concise and clear information. Twitter users responding to the 2024 presidential candidate have different opinions on Twitter. The sentiments used are positive, negative and neutral. The method used to analyze public opinion with data processed on Twitter social media uses Extreme Gradient Boosting (XGBOOST), classifying tweet test data in the form of classification with prediction output with accurate values. This research takes Twitter data to see public opinion on presidential candidates. The aim of this research is to determine the process of digital text analysis and the application of the XGBOOST method to Twitter user sentiment in two categories (positive and negative) and three categories (positive, negative and neutral) for each candidate, namely Ganjar Pranowo, Anies Baswedan and Prabowo Subianto. The results show an accuracy of 0.96%, precision of 0.96% and recall of 0.97%.

Jurnal Informatika Jurnal Pengembangan ITVol. 9(3)
Universitas Teknokrat Indonesia (ID)
Peace, Justice and strong institutions
Openalex Percentile: Top 61%
Data Mining and Machine Learning Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.