What type of study is this?

This is a Quantitative Study study.

November 4, 2025TeknikaOpen Access

Performance Comparison of 10 Machine Learning Algorithms in Sentiment Classification on Platform X Regarding the Government’s Priority Program: Makanan Bergizi Gratis (MBG)

View Full Paper

Ask AI

Bookmark

Authors

DUDeni UtamaState University of Jakarta RFRauhil FahmiState University of Jakarta MPMuhammad Ridho Kurniawan PratamaState University of Jakarta

Discussion

Member takes

Implication

Analysis demonstrates high accuracy in sentiment classification on government food program comments using machine learning methodologies, suggesting some insights for policy enhancement.

Key Points

Bagging achieved the highest accuracy at 81% in the sentiment classification task, indicating robust performance among the tested algorithms.
The evaluation was conducted using models like Naive Bayes and Gradient Boosting, with metrics such as precision and recall guiding the assessment.
Data preprocessing included tokenization and TF-IDF vectorization, ensuring the algorithms were optimized for sentiment analysis.
Findings may inform strategies for improving government communication and enhancing public perception of food programs.

Cite This Study

Utama et al. (2025) studied this question.

synapsesocial.com/papers/690945348f2297dc13532e8d https://doi.org/10.34148/teknika.v14i3.1386

View Full Paper

Ask AI

Bookmark

Implication

Analysis demonstrates high accuracy in sentiment classification on government food program comments using machine learning methodologies, suggesting some insights for policy enhancement.

Performance Comparison of 10 Machine Learning Algorithms in Sentiment Classification on Platform X Regarding the Government’s Priority Program: Makanan Bergizi Gratis (MBG)

Discussion

Member takes

Implication

Key Points

Cite This Study

Performance Comparison of 10 Machine Learning Algorithms in Sentiment Classification on Platform X Regarding the Government’s Priority Program: Makanan Bergizi Gratis (MBG)

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider