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      "abstract": "Layanan streaming seperti Disney Hotstar populer di Indonesia, namun\r\nfluktuasi kepuasan pengguna terlihat dari ulasan di Google Play Store yang\r\nmengandung sentimen positif dan negatif, sering kali tidak seimbang. Masalah\r\nketidakseimbangan data ini dapat menyebabkan bias model klasifikasi, sehingga\r\ndiperlukan analisis sentimen untuk mengidentifikasi kecenderungan opini\r\npengguna guna perbaikan aplikasi. Penelitian ini bertujuan mengoptimalkan\r\npipeline analisis sentimen untuk mengklasifikasikan ulasan positif-negatif secara\r\nakurat.\r\nMetode penelitian meliputi pengumpulan data ulasan Disney Hotstar, prapengolahan\r\n(cleansing,\r\nnormalisasi,\r\ncase\r\nfolding,\r\ntokenizing,\r\nfiltering),\r\npelabelan\r\n\r\nberdasarkan\r\n\r\nrating (positif: 4-5, negatif: 1-2), pembobotan TF-IDF, penerapan\r\nSMOTE untuk oversampling kelas minoritas, dan klasifikasi SVM dengan kernel\r\nlinear serta RBF menggunakan K-Fold Cross Validation. Implementasi dilakukan\r\ndengan Python dan scikit-learn, membandingkan skenario dengan dan tanpa\r\nSMOTE serta feature selection. Alur ini mengoptimalkan performa model pada\r\ndataset imbalance.\r\nHasil menunjukkan akurasi model SVM mencapai sekitar 83% dengan F1score\r\n\r\nmacro 0.82-0.83, stabil meski data tidak seimbang (positif ~700, negatif\r\n~1100). SMOTE tidak signifikan meningkatkan performa, dan kernel RBF sedikit\r\nlebih baik daripada linear. Model ini bermanfaat bagi developer Disney Hotstar\r\nuntuk analisis ulasan dan akademisi sebagai referensi optimasi SVM pada teks\r\nIndonesia; saran lanjutan termasuk uji algoritma lain seperti XGBoost.",
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      "uri": "http:\/\/eprints.amikom.ac.id\/id\/eprint\/33155",
      "institution": "Universitas AMIKOM Yogyakarta",
      "creators": [
        {
          "name": {
            "honourific": null,
            "given": "Hasymi",
            "family": "Al Hamdi",
            "lineage": null
          },
          "nim": "19.11.2997"
        }
      ],
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      "ispublished": "pub",
      "keywords": "Sentiment analysis, SVM, SMOTE, TF-IDF, Disney Hotstar, Analisis sentimen, Disney.",
      "type": "thesis",
      "contributors": [
        {
          "name": {
            "honourific": null,
            "lineage": null,
            "family": "Dhuhita",
            "given": "Windha Mega Pradnya"
          }
        }
      ],
      "date": "2026-02-27",
      "status_changed": "2026-10-06 02:01:41",
      "full_text_status": "restricted",
      "divisions": [
        "if"
      ],
      "department": "Fakultas Ilmu Komputer",
      "rev_number": 16,
      "title": "ANALISIS SENTIMEN DENGAN ALGORITMA SUPPORT VECTOR MACHINE\r\nTERHADAP ULASAN APLIKASI DISNEY+ HOTSTAR DI PLAY STORE"
    }