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      "institution": "Universitas AMIKOM Yogyakarta",
      "abstract": "Penyakit paru-paru merupakan salah satu penyebab utama morbiditas dan\r\nmortalitas di dunia, sehingga deteksi dini memiliki peran penting dalam\r\nmeningkatkan akurasi diagnosis dan efektivitas penanganan medis. Citra rontgen\r\ndada (Chest X-ray) merupakan modalitas pencitraan yang paling umum digunakan\r\nuntuk mendeteksi berbagai penyakit paru, namun proses interpretasinya masih\r\nsangat bergantung pada keahlian radiolog dan berpotensi menimbulkan\r\nsubjektivitas, terutama pada kasus dengan lebih dari satu kondisi patologis dalam\r\nsatu citra. Kompleksitas ini dapat berdampak pada keterlambatan diagnosis dan\r\nketidakkonsistenan hasil interpretasi. Oleh karena itu, penelitian ini bertujuan untuk\r\nmengembangkan dan mengevaluasi pendekatan deteksi otomatis penyakit paru\r\nberbasis deep learning menggunakan arsitektur transformer, yaitu Vision\r\nTransformer (ViT) dan Swin Transformer. Penelitian dilakukan menggunakan\r\ndataset CheXpert-v1.0-small yang terdiri dari citra X-ray dada dengan 12 label\r\npenyakit paru yang relevan. Tahapan penelitian meliputi pembersihan dan prapemrosesan\r\ndata,\r\npenanganan\r\nlabel\r\ntidak\r\npasti,\r\npembagian\r\ndata\r\nmenjadi\r\ndata\r\nlatih,\r\n\r\nvalidasi,\r\ndan\r\nuji,\r\nserta\r\npelatihan\r\nmodel\r\ndalam\r\ndua\r\nskenario,\r\nyaitu\r\nbaseline\r\n(non\r\nfinetuning)\r\n\r\ndan fine-tuning. Evaluasi kinerja model dilakukan menggunakan metrik\r\nArea Under the Curve – Receiver Operating Characteristic (AUC-ROC) sebagai\r\nindikator akurasi klasifikasi multi-label. Hasil penelitian menunjukkan bahwa\r\npenerapan fine-tuning mampu meningkatkan performa kedua model secara\r\nkonsisten, di mana Swin Transformer hasil fine-tuning menunjukkan kinerja paling\r\nstabil dengan nilai AUC berada pada rentang 0,75–0,88 pada sebagian besar label\r\npenyakit, termasuk pneumonia. Penelitian ini diharapkan dapat dimanfaatkan\r\nsebagai dasar pengembangan sistem pendukung keputusan klinis berbasis\r\nkecerdasan buatan untuk membantu proses diagnosis penyakit paru secara lebih\r\ncepat dan objektif.",
      "title": "EKSPERIMEN MODEL VISION TRANSFORMER DAN SWIN\r\nTRANSFORMER BERBASIS FINE-TUNING UNTUK MULTILABEL\r\nMULTI-KRITERIA\r\nPADA\r\nCITRA\r\nX-RAY\r\nPARUPARU",
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