EPrints Universitas Amikom YogyakartaOPTIMASI SUPPORT VECTOR MACHINE MENGGUNAKAN SMOTE UNTUK MENANGANI KETIDAKSEIMBANGAN DATA PADA KLASIFIKASI SENTIMENAji NugrohoAsshidiqauthorAnalisis sentimen seringkali menghadapi masalah ketidakseimbangan kelas data (imbalanced dataset) yang menyebabkan model Support Vector Machine (SVM) cenderung bias dan kesulitan memprediksi kelas minoritas. Penelitian ini bertujuan untuk mengoptimalkan kinerja SVM dalam mengklasifikasikan data sentimen menggunakan teknik penyeimbangan data Synthetic Minority Over-sampling Technique (SMOTE) dan metode pembobotan teks Term Frequency-Inverse Document Frequency (TF-IDF). Pengujian dilakukan melalui tiga rasio pembagian data latih dan uji, yaitu 80:20 (Skenario 1), 70:30 (Skenario 2), dan 60:40 (Skenario 3). Peningkatan paling terlihat ketika menguji skenario 80:20(Skenario 1), dimana kinerja model mencapai titik paling optimal dengan peningkatan rata-rata akurasi sebesar 82,83% dan F1-Score 82,42%. Kesimpulannya, penerapan SMOTE terbukti sangat efektif untuk mengatasi bias terhadap kelas mayoritas, sehingga menghasilkan sistem klasifikasi sentimen yang jauh lebih adil, stabil, dan dapat diandalkan.005 Pemrograman komputer, program dan data2026-02-26Universitas AMIKOM Yogyakarta;Fakultas Ilmu KomputerThesis

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