<![CDATA[The quality of telemedicine platforms can be assessed through sentiment analysis of user opinions on social media. This study compared two hybrid methods, InSet-SVM and SentiStrength_id-SVM, for sentiment analysis of the Halodoc and Alodokter platforms. Both lexicons were selected because they apply similar sentiment-weighting mechanisms but differ in vocabulary coverage. Data were collected from X (formerly Twitter) using TWINT with the keywords “Halodoc” and “Alodokter” from January 1 to December 31, 2022. The data were labeled using each sentiment lexicon, while TF-IDF was applied for feature weighting before classification using a Support Vector Machine (SVM). The models were evaluated using three-times-repeated 10-fold cross-validation. The results showed that InSet-SVM outperformed SentiStrength_id-SVM across all evaluation metrics for both datasets. For Halodoc, InSet-SVM achieved 85.92% precision, 86.24% recall, an F1-score of 85.76%, and 86.24% accuracy. For Alodokter, it achieved 83.86% precision, 84.28% recall, an F1-score of 83.59%, and 84.28% accuracy. These findings indicate that using InSet as the initial labeling lexicon produces a more consistent SVM model across both datasets.]]>
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