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      "abstract": "Analisis sentimen seringkali menghadapi masalah ketidakseimbangan kelas data\r\n(imbalanced dataset) yang menyebabkan model Support Vector Machine (SVM)\r\ncenderung bias dan kesulitan memprediksi kelas minoritas. Penelitian ini bertujuan\r\nuntuk mengoptimalkan kinerja SVM dalam mengklasifikasikan data sentimen\r\nmenggunakan teknik penyeimbangan data Synthetic Minority Over-sampling\r\nTechnique (SMOTE) dan metode pembobotan teks Term Frequency-Inverse\r\nDocument Frequency (TF-IDF).  Pengujian dilakukan melalui tiga rasio pembagian\r\ndata latih dan uji, yaitu 80:20 (Skenario 1), 70:30 (Skenario 2), dan 60:40 (Skenario\r\n3). Peningkatan paling terlihat ketika menguji skenario 80:20(Skenario 1), dimana\r\nkinerja model mencapai titik paling optimal dengan peningkatan rata-rata akurasi\r\nsebesar 82,83% dan F1-Score 82,42%. Kesimpulannya, penerapan SMOTE\r\nterbukti sangat efektif untuk mengatasi bias terhadap kelas mayoritas, sehingga\r\nmenghasilkan sistem klasifikasi sentimen yang jauh lebih adil, stabil, dan dapat\r\ndiandalkan.",
      "metadata_visibility": "show",
      "title": "OPTIMASI SUPPORT VECTOR MACHINE MENGGUNAKAN\r\nSMOTE UNTUK MENANGANI KETIDAKSEIMBANGAN\r\nDATA PADA KLASIFIKASI SENTIMEN",
      "dir": "disk0\/00\/03\/23\/90",
      "institution": "Universitas AMIKOM Yogyakarta",
      "subjects": [
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          "name": {
            "given": "Aji Nugroho",
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            "honourific": null,
            "family": "Asshidiq"
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      "rev_number": 15,
      "department": "Fakultas Ilmu Komputer",
      "date": "2026-02-26",
      "status_changed": "2026-07-27 07:13:24",
      "ispublished": "pub",
      "type": "thesis",
      "date_type": "published",
      "thesis_type": "bachelor",
      "full_text_status": "restricted",
      "uri": "http:\/\/eprints.amikom.ac.id\/id\/eprint\/32390",
      "keywords": "Analisis Sentimen, Support Vector Machine (SVM), Imbalanced\r\nDataset, SMOTE, TF-IDF, Sentiment Analysis."
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