Abstract
This study addresses the challenge of sentiment analysis within the Information Technology study program at Universitas Timor, aiming to compare the performance of Support Vector Machines (SVM) and Naive Bayes (NB) through 100 iterations. The dataset, comprising 21 instances of negative sentiment and 18 instances of positive sentiment, is analyzed using both methods, with accuracy and Area Under the ROC Curve (AUC) serving as key metrics. The sample size consists of 39 instances, and the results indicate significant variability in both accuracy and AUC, emphasizing the sensitivity of the models to dataset characteristics and random initialization. On average, SVM outperforms NB, with an accuracy of 0.5846 compared to 0.5075 and an AUC of 0.5916 compared to 0.4607.
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