<![CDATA[This research has the potential to make an important contribution to the development of computationally-based sentiment analysis, especially in the context of government policies regarding the Free Meal Program that will be implemented throughout Indonesia. This research was conducted using Indo-BERT and Bi-LSTM algorithms. These approaches were used to categorize emotions into three groups: neutral, negative, and positive. Data is obtained from posts on social media X, then after processing the data, it will be applied to both algorithms, namely Indo-BERT and Bi-LSTM. The research findings show that the model's performance in determining the public sentiment of government policies. Validation and valuation were conducted using the f1 score, recall, and precision metrics. The evaluation findings show that the Indo-BERT algorithm is better than the Bi-LSTM algorithm with an accuracy value of 80% for Indo-BERT and 78% for the accuracy value of the Bi-LSTM algorithm, and the Indo-BERT accuracy value is included in the good classification accuracy value. The sentiment analysis results are also represented by word clouds for each positive, negative and neutral class, providing an intuitive picture of the words frequently used in public discourse on free nutritious meals.]]>
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