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      "uri": "http:\/\/eprints.amikom.ac.id\/id\/eprint\/32244",
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      "keywords": "Tumor Otak, Deep Learning, Transfer Learning, ResNet50, VGG16, Brain Tumor.",
      "rev_number": 16,
      "ispublished": "pub",
      "status_changed": "2026-07-20 03:38:12",
      "type": "thesis",
      "date": "2026-02-18",
      "department": "Fakultas Ilmu Komputer",
      "corp_creators": [
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      "subjects": [
        "000.000.000A",
        "000.000.006"
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        {
          "name": {
            "honourific": null,
            "family": "Tanaraw",
            "lineage": null,
            "given": "Gillas"
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          "nim": "22.11.4858"
        }
      ],
      "title": "PERBANDINGAN ARSITEKTUR DEEP LEARNING\r\nRESNET50 DAN VGG16 DALAM DETEKSI TUMOR OTAK\r\nMENGGUNAKAN CITRA MRI",
      "abstract": "Tumor otak merupakan salah satu penyakit berbahaya yang dapat\r\nmengganggu fungsi sistem saraf pusat dan memerlukan deteksi dini untuk\r\nmeningkatkan peluang penanganan yang tepat. Magnetic Resonance Imaging\r\n(MRI) menjadi salah satu metode pemeriksaan yang banyak digunakan karena\r\nmampu menghasilkan citra otak dengan detail tinggi. Namun, proses identifikasi\r\ntumor melalui citra MRI secara manual membutuhkan waktu, ketelitian, serta\r\nbergantung pada pengalaman tenaga medis, sehingga berpotensi menimbulkan\r\nperbedaan interpretasi. Penelitian ini bertujuan untuk membangun model\r\nklasifikasi citra MRI otak menjadi dua kelas, yaitu “yes” (tumor) dan “no” (tidak\r\ntumor), menggunakan pendekatan deep learning berbasis transfer learning. Model\r\nyang digunakan adalah ResNet50 dan VGG16 dengan bobot awal ImageNet.\r\nDataset citra MRI dikonversi ke dalam format CSV metadata yang berisi path file,\r\nukuran gambar, ukuran file, dan label untuk memudahkan proses pengolahan data.\r\nData kemudian dibagi menjadi train, validation, dan test. Tahapan pelatihan\r\ndilakukan menggunakan metode fine-tuning untuk meningkatkan performa model.\r\nEvaluasi dilakukan menggunakan metrik loss, accuracy, precision, recall, f1score,\r\ndan\r\nROC-AUC.\r\nHasil\r\npenelitian\r\nmenunjukkan\r\nbahwa\r\npendekatan\r\ntransfer\r\n\r\nlearning\r\n\r\nmampu memberikan performa klasifikasi yang baik pada dataset dua\r\nkelas, serta dapat digunakan sebagai dasar pengembangan sistem pendukung\r\ndiagnosis tumor otak berbasis komputer untuk membantu proses analisis citra\r\nMRI secara lebih cepat dan konsisten.",
      "dir": "disk0\/00\/03\/22\/44",
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      "datestamp": "2026-07-20 03:38:12",
      "contributors": [
        {
          "name": {
            "lineage": null,
            "given": "Afrig",
            "honourific": null,
            "family": "Aminuddin"
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