Penerapan SVM dan Regresi untuk Prediksi Intensitas Sentimen Pemilu Presiden Indonesia
DOI:
https://doi.org/10.22441/incomtech.v15i3.28525Keywords:
Analisis Sentimen, Machine Learning, Pemilihan Umum, Media SosialAbstract
Dalam konteks pemilihan umum presiden Indonesia, analisis sentimen publik melalui media sosial merupakan alat yang penting untuk memahami persepsi dan reaksi masyarakat terhadap calon presiden dan kebijakan mereka. Studi ini mengembangkan model hybrid yang mengintegrasikan Support Vector Machine (SVM) dan Ridge Regression, menggunakan library BERT untuk memprediksi intensitas sentimen dari data Twitter. Pendekatan ini dirancang untuk mengatasi tantangan variabilitas ekspresi dan ambiguitas bahasa, yang sering kali mempersulit interpretasi data sentimen dengan tepat. Penelitian ini menggunakan teknik preprocessing yang komprehensif, termasuk pembersihan teks dan normalisasi data, serta penerapan teknik Synthetic Minority Over-sampling Technique (SMOTE) untuk menangani ketidakseimbangan kelas dalam dataset. Hasil dari penelitian ini menunjukkan bahwa model hybrid dapat mencapai tingkat akurasi, presisi, recall, dan F1-Score yang tinggi dengan tiga rasio yang berbeda, menegaskan keefektifan model dalam mengklasifikasikan dan mengukur intensitas sentimen. Temuan menunjukkan bahwa kombinasi SVM dan regresi, didukung dengan analisis BERT, efektif dalam mengklasifikasikan dan mengukur intensitas sentimen secara akurat. Hasil intensitas yang dijelaskan pada gambar 11 untuk kandidat Anies Baswedan mayoritas sentimen adalah netral sebesar 53.1%. Selanjutnya, pada gambar 12 untuk kandidat Prabowo Subianto netral sebesar 63.5% dan gambar 13 untuk kandidat Ganjar Pranowo dengan 62.9%.Downloads
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