Penelitian ini menganalisis sentimen terhadap program makan siang dan susu gratis Prabowo-Gibran menggunakan Support Vector Machine (SVM). Sebanyak 1308 tweet diambil dari Twitter dalam rentang waktu April hingga Juni 2024 menggunakan teknik crawling dengan Tweepy. Setelah preprocessing, 1025 tweet tersisa untuk analisis. Proses preprocessing mencakup case folding, cleansing, stopword removal, normalisasi, stemming, dan tokenisasi. Data diberi label sentimen positif (10.35%), netral (13.77%), dan negatif (75.88%), kemudian dihitung tf-idf. Model SVM dengan 70% data training dan 30% data testing menunjukkan akurasi 82.47%, dengan hasil presisi, recall, dan f1-score yang bervariasi untuk setiap kategori sentimen.

This research analyzes the sentiment towards the Prabowo-Gibran free lunch and milk program using Support Vector Machine (SVM). A total of 1308 tweets were retrieved from Twitter from April to June 2024 using crawling technique with Tweepy. After preprocessing, 1025 tweets remained for analysis. The preprocessing process included case folding, cleansing, stopword removal, normalization, stemming, and tokenization. The data was labeled with positive (10.35%), neutral (13.77%), and negative (75.88%) sentiments, then tf idf was calculated. The SVM model with 70% training data and 30% testing data showed 82.47% accuracy, with varying precision, recall, and f1-score results for each sentiment category.