Sentiment Analysis on the Allocation of the MBG Program Budget Using Support Vector Machine
-
Aulia Kartika Dewi
*
Universitas Islam Negeri Sumatera Utara, Medan,
Indonesia
- Raissa Amanda Putri Universitas Islam Negeri Sumatera Utara, Medan, Indonesia (*) Corresponding Author
Abstract
Sentiment analysis is one of the applications of artificial intelligence and machine learning used to automatically identify and classify public opinions, particularly those expressed on social media. This approach is important for understanding public perceptions of a policy, as it provides a systematic, fast, and data-driven overview. With the increasing use of social media, sentiment analysis can be utilized as an evaluation tool to support transparency and more objective decision-making. One issue that has attracted public attention is the MBG (Free Nutritious Food) Program, a government initiative aimed at improving community nutrition. The budget allocation for this program has generated various responses from the public, including both support and criticism regarding its implementation and policy priorities. Therefore, an analysis that can comprehensively capture these diverse perspectives is necessary. This study aims to analyze public sentiment toward the MBG Program budget using data from the social media platform X (Twitter), which is known for its ability to represent real-time and dynamic public opinion. The dataset collected through crawling consists of 2,487 entries, and after preprocessing, 1,686 valid data points were obtained for analysis. Feature extraction was performed using the TF-IDF method, while sentiment classification was conducted using the Support Vector Machine (SVM) algorithm. Model evaluation was carried out using 5-Fold Cross Validation and Confusion Matrix. The results show that the developed model achieved an accuracy of 81.17%, indicating good performance in sentiment classification. For the negative class, the precision reached 85.48% and recall 98.76%. For the neutral class, the precision was 57.58%, recall 44.19%, and F1-score 49.98%. For the positive class, the precision was 75.00%, recall 15.79%, and F1-score 26.09%. These findings indicate that a machine learning-based approach can contribute to understanding public opinion and support more effective, data-driven government policy evaluation. This study contributes by demonstrating the effectiveness of the SVM algorithm in classifying public sentiment on policy-related issues, as well as by applying k-fold cross-validation and confusion matrix to provide a more comprehensive and reliable evaluation. The findings are expected to support data-driven policy evaluation and enhance understanding of public opinion toward government programs.
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