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            "lineage": null,
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            "given": "Nadiyatulhusna",
            "family": "-"
          }
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      "rev_number": 14,
      "title": "ANALISIS KOMPARATIF VISION TRANSFORMER (VIT)\r\nMENGGUNAKAN HYBRID FINE-TUNING–XGBOOST \r\nUNTUK KLASIFIKASI PENYAKIT KATARAK \r\nBERBASIS CITRA MATA",
      "thesis_name": "skripsi",
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      "thesis_type": "bachelor",
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      "institution": "Universitas AMIKOM Yogyakarta",
      "metadata_visibility": "show",
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      "date": "2026-05-25",
      "full_text_status": "restricted",
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      ],
      "contributors": [
        {
          "name": {
            "honourific": null,
            "lineage": null,
            "family": "Abdulloh",
            "given": "Ferian Fauzi"
          }
        }
      ],
      "date_type": "published",
      "abstract": "Penyakit katarak merupakan penyebab utama kebutaan yang dapat dicegah\r\njika didiagnosis secara dini. Namun, pemeriksaan manual citra fundus seringkali\r\nmemakan waktu dan bergantung pada keahlian spesialis. Penelitian ini bertujuan\r\nmengembangkan dan menganalisis secara komparatif model klasifikasi citra\r\nkatarak menggunakan arsitektur Vision Transformer (ViT) dengan pendekatan\r\nhibrida fine-tuning dan eXtreme Gradient Boosting (XGBoost). Eksperimen\r\nmenggunakan data sekunder dari repositori Ocular Disease Intelligent Recognition\r\n(ODIR) yang dibagi dengan metode Holdout (80% data latih, 20% data uji murni).\r\nUntuk mengatasi data tidak seimbang dan mencegah overfitting, skema Stratified\r\n5-Fold Cross Validation dikombinasikan dengan Random Oversampling (ROS)\r\nsecara internal pada setiap lipatan. Skenario penelitian membandingkan model ViT\r\nmurni end-to-end (baseline) dengan model Hybrid ViT-XGBoost, di mana ViT\r\nbertindak sebagai pengekstraksi fitur laten 768-dimensi dari token CLS dan\r\nXGBoost sebagai pengklasifikasi akhir berbasis decision tree ensemble. Hasil\r\npengujian pada 684 sampel data uji murni menunjukkan model Hybrid ViTXGBoost\r\nmemberikan\r\nperforma\r\nterbaik\r\ndengan\r\nAkurasi\r\nglobal\r\n97%, Presisi\r\nkelas\r\n\r\nkatarak\r\n0,85,\r\nRecall\r\n0,84,\r\ndan\r\nF1-Score\r\n0,85.\r\nSebaliknya,\r\nViT\r\nmurni\r\nmenghasilkan\r\n\r\nAkurasi\r\nlebih\r\nrendah\r\n(96%)\r\ndan\r\nPresisi\r\nkatarak\r\njatuh\r\ndi\r\nangka\r\n0,75\r\nakibat\r\n18\r\nkasus\r\n\r\nFalse\r\n\r\nPositive. Hambatan atensi global ViT terhadap noise visual (artifak\r\npencahayaan dan debu lensa sebesar 8,93% dari data) berhasil diredam oleh batas\r\nkeputusan non-linear XGBoost. Penelitian ini membuktikan model hibrida ini\r\nmenghasilkan diagnosis yang lebih selektif dan andal untuk diimplementasikan\r\npada sistem pendukung keputusan klinis masa depan bagi praktisi medis.",
      "userid": 12,
      "eprintid": 33052,
      "department": "Fakultas Ilmu Komputer",
      "lastmod": "2026-09-29 04:49:35",
      "subjects": [
        "000.000.000A"
      ]
    }