<![CDATA[This study aims to analyze public sentiment towards the boycott of pro-Israel products on social media using machine learning. Data was collected through crawling on Instagram tweets and comments, then processed through preprocessing stages such as cleaning, tokenizing, normalizing, stopword removal, and stemming. The analysis was carried out using four machine learning algorithms, namely Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree. The results showed that SVM provided the highest accuracy in sentiment classification. Positive sentiment dominated, in the form of support for the boycott movement as humanitarian solidarity for Palestine, while negative sentiment included the view that this movement was ineffective and potentially detrimental to the economy. A comparison of social media shows that Twitter, with its real-time nature, tends to present fast, emotional, and argument-based responses. In contrast, Instagram focuses more on visual content such as infographics and short videos, with more passive discussions in the comments column. This study shows that sentiment analysis on social media can be an important tool for businesses to understand public perceptions of sensitive issues, detect potential crises, and design more effective communication strategies.]]>
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