<![CDATA[The century-long Israeli-Palestinian conflict has created diverse opinions in Indonesian society. The escalation of tensions in Gaza triggered calls for boycotts of products suspected of supporting Israel. In this study, a Support Vector Machine (SVM) method is used to analyze sentiment on Twitter related to pro-Israel boycotts. By understanding public opinion, this study evaluates the performance of SVM with linear kernel and RBF. Data collection was done through crawling Twitter with the keyword "Pro-Israel boycott", resulting in 2600 data. Data preprocessing involved case folding, cleaning, stopwords, stemming, and TF-IDF weighting. Manual labeling was done for 1560 support data and 1040 non-support data. Implementation of the SVM model resulted in 92.5% accuracy for the linear kernel and 91.92% for the RBF kernel. Word cloud analysis provided visualization of key words and sentiments related to the boycott. This research shows the dominance of positive sentiment with 1560 positive tweets and 1040 negative tweets. For development, it is recommended to add sentiment analysis methods, use a wider dataset, and consider supporting variables to improve accuracy and understanding of public sentiment on the issue.]]>
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