Klasifikasi Sentimen Terhadap Pengangkatan Kaesang Sebagai Ketua Umum Partai PSI Menggunakan Metode Support Vector Machine
- Safrizal .Safrizal Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
-
Surya Agustian
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Alwis Nazir Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Yusra Yusra Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
The appointment of Kaesang Pangarep as the Chairman of the Indonesian Solidarity Party (PSI) has sparked various responses on social media, particularly on Twitter. This research aims to classify public sentiment regarding the appointment using the Support Vector Machine (SVM) algorithm with FastText feature representation. The data used for classification involves a small training dataset. The text preprocessing process includes cleaning, case folding, tokenizing, normalization, stopword removal, and stemming. FastText word embedding is used to convert words into vectors, and an SVM model with Grid Search is used for parameter tuning to obtain the optimal model. The use of external datasets to expand the initially limited training dataset enhances data representation and improves the model's performance in sentiment classification. The Covid dataset was expanded by adding 100, 200, and 300 tweets for each negative, positive, and neutral label. From the experiments conducted, the best accuracy on the test data was found in experiment ID C2 with an F1-Score of 53.59% and an accuracy of 62.73%. In experiment ID C3 with the same dataset, the F1-Score was 50.46% and the accuracy was 60.46%. Finally, in experiment ID C7 with the same dataset, the F1-Score was 47.19% and the accuracy was 53.09%.
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References
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