Sentiment Analysis of Student Advisory Services at UMSIDA Using the Support Vector Machine (SVM) Method
Analisis Sentimen Layanan Perwalian Mahasiswa UMSIDA Menggunakan Metode Support Vector Machine (SVM)
DOI:
https://doi.org/10.21070/ups.7397Keywords:
Analysis Sentiment, Counseling guidance, SVM, TF-IDFAbstract
The myUMSIDA application supports academic activities at Universitas Muhammadiyah Sidoarjo, but student reviews indicate complaints about its services and facilities. Sentiment analysis is needed to classify these reviews as positive or negative, providing insights for service improvement. This study applies the Support Vector Machine (SVM) algorithm with a linear kernel, known for its high accuracy, combined with the TF-IDF feature extraction method for better text classification. A total of 1,300 reviews from 2023 underwent labeling, preprocessing, transformation, and classification. The dataset was split into three training-testing scenarios: 70:30, 60:40, and 50:50.Performance evaluation showed that the 70:30 scenario yielded the best results, with 86.92% accuracy, 86.60% precision, 84.72% recall, and an 85.7% F1-Score. These findings demonstrate the effectiveness of SVM with a linear kernel and TF-IDF in sentiment analysis, offering a foundation for improving myUMSIDA’s services.
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