This study presents a sentiment analysis of the 2024 Indonesian presidential election using the Support Vector Machine (SVM) algorithm with Word2Vec feature extraction. The research aims to analyze public sentiment towards the three main candidates: Anies Baswedan, Prabowo Subianto, and Ganjar Pranowo, based on tweets from Twitter. The dataset consists of 14,318 Indonesian tweets, categorized into positive and negative sentiments. The study employs preprocessing steps including tokenization, stopword removal, and stemming, followed by feature extraction using Word2Vec. The SVM algorithm is used for classification, and the performance is evaluated using precision, recall, F1-score, and accuracy. The results show that the SVM algorithm with Word2Vec feature extraction achieves a precision of 88.94%, recall of 93.08%, F1-score of 90.43%, and accuracy of 90.75% when using an 80:20 data split ratio. This performance is higher than previous studies using SVM, which achieved an accuracy of 82.3%. The sentiment analysis reveals that Prabowo Subianto received the highest positive sentiment (41%), while Anies Baswedan had the highest negative sentiment (64%). Ganjar Pranowo had the highest positive sentiment (36%) and the second-highest negative sentiment (25%). The study concludes that the SVM algorithm with Word2Vec feature extraction is effective for sentiment analysis in the context of the 2024 Indonesian presidential election.This study presents a sentiment analysis of the 2024 Indonesian presidential election using the Support Vector Machine (SVM) algorithm with Word2Vec feature extraction. The research aims to analyze public sentiment towards the three main candidates: Anies Baswedan, Prabowo Subianto, and Ganjar Pranowo, based on tweets from Twitter. The dataset consists of 14,318 Indonesian tweets, categorized into positive and negative sentiments. The study employs preprocessing steps including tokenization, stopword removal, and stemming, followed by feature extraction using Word2Vec. The SVM algorithm is used for classification, and the performance is evaluated using precision, recall, F1-score, and accuracy. The results show that the SVM algorithm with Word2Vec feature extraction achieves a precision of 88.94%, recall of 93.08%, F1-score of 90.43%, and accuracy of 90.75% when using an 80:20 data split ratio. This performance is higher than previous studies using SVM, which achieved an accuracy of 82.3%. The sentiment analysis reveals that Prabowo Subianto received the highest positive sentiment (41%), while Anies Baswedan had the highest negative sentiment (64%). Ganjar Pranowo had the highest positive sentiment (36%) and the second-highest negative sentiment (25%). The study concludes that the SVM algorithm with Word2Vec feature extraction is effective for sentiment analysis in the context of the 2024 Indonesian presidential election.