<![CDATA[The classification of public sentiment towards Ganjar Pranowo on Twitter can provide insights into his popularity, support, or criticism. This research aims to classify public sentiment towards Ganjar Pranowo on Twitter using the Support Vector Machine (SVM) method. The research data consists of 4000 tweets collected from Twitter. After undergoing preprocessing, these tweets are classified using SVM into positive or negative classes. The classification method is optimized to produce the most optimal model by testing the influence of feature selection stages and SVM parameter tuning. The data is divided into 80% training (TRAIN_SET) and 20% testing (TEST_SET). The optimal model is validated using 10% of the randomly selected TRAIN_SET for validation data. Sixteen experiments are conducted to explore the optimal model, with the highest validation results (top rank 4 models) tested on the TEST_SET, yielding F1-scores of 84.13%, 84.13%, 84.13%, and 84.13% for experiment IDs 1, 7, 14, and 16, respectively. In this research, SVM proves to be sufficiently effective in classifying sentiment-related tweets about Ganjar Pranowo on Twitter]]>
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