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Yerik Afrianto Singgalen


This study investigates the performance of the Support Vector Machine (SVM) algorithm in sentiment analysis tasks within the context of tourism destination branding, utilizing the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. Specifically, the research compares SVM performance with and without the Synthetic Minority Over-sampling Technique (SMOTE) across various metrics including accuracy, precision, recall, F-measure, and Area Under the Curve (AUC). The analysis is conducted on a dataset comprising textual data extracted from "Wonderful Indonesia" promotional videos featuring Labuan Bajo. Results indicate that SVM without SMOTE achieves a slightly higher accuracy of 97.79% compared to 96.61% with SMOTE. However, a closer examination reveals that SVM without SMOTE accurately classifies all positive instances, while with SMOTE, one positive instance is misclassified as negative. Precision, recall, and F-measure scores for positive instances are also higher without SMOTE, indicating better performance in classifying positive sentiment


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Singgalen, Y. A. (2024) “Performance evaluation of SVM with synthetic minority over-sampling technique in sentiment classification”, Jurnal Mantik, 8(1), pp. 326-336. doi: 10.35335/mantik.v8i1.5077.
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