Analisis Sentimen Program Makan Gratis Pada Media Sosial X Menggunakan Metode Naïve Bayes
DOI:
https://doi.org/10.47065/jimat.v6i1.963Keywords:
Sentiment Analysis; Free Nutritious Meal Program; Social Media X; TF-IDF; Naive BayesAbstract
The Free Meal Program is a public policy aimed at improving students' nutritional needs. However, the program's implementation has generated diverse responses from the public. This study aims to analyze public sentiment in Medan Tuntungan District towards the Free Meal Program based on opinion data obtained from social media platform X. The methods used include text preprocessing, word weighting using Term Frequency–Inverse Document Frequency (TF-IDF), and sentiment classification using the Naïve Bayes algorithm. The research data was divided into training and test data with a ratio of 80% and 20%. The analysis results indicate that public sentiment is dominated by negative sentiment, generally related to food quality, portion adequacy, and program budget management. Model performance evaluation indicates that the Naïve Bayes algorithm is able to identify trends in public sentiment, although the accuracy obtained still shows limitations in classification performance. These findings provide an empirical overview of public perception of the Free Meal Program at the local level and serve as a basis for developing more adaptive sentiment analysis methods in future research.
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