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      "abstract": "Penelitian ini bertujuan untuk mengimplementasikan arsitektur\r\nMobileNetV3 pada tugas klasifikasi spesies bunga serta menganalisis perbandingan\r\nperforma model dengan dan tanpa penerapan augmentasi data serta fine-tuning.\r\nMobileNetV3 dikenal sebagai model yang ringan dan efisien untuk perangkat\r\ndengan sumber daya terbatas, sehingga cocok untuk aplikasi klasifikasi citra secara\r\nreal-time. Fokus utama studi ini adalah mengukur sejauh mana kombinasi\r\naugmentasi data dan fine-tuning dapat meningkatkan akurasi model serta\r\npengaruhnya terhadap efisiensi komputasi. \r\nMetode penelitian dilakukan dengan melatih dua konfigurasi model\r\nmenggunakan dataset Oxford Flowers 102. Model pertama digunakan sebagai\r\nbaseline tanpa augmentasi data dan hanya mengandalkan bobot awal pre-trained\r\ntanpa fine-tuning menyeluruh, sedangkan model kedua dioptimalkan dengan teknik\r\naugmentasi data (seperti rotasi, flipping, dan penyesuaian kontras) serta fine-tuning\r\npada lapisan tertentu. Evaluasi dilakukan berdasarkan metrik akurasi (top-1\r\naccuracy) dan efisiensi komputasi yang diukur dari waktu inferensi rata-rata serta\r\njumlah parameter model. \r\nHasil eksperimen menunjukkan bahwa model dengan augmentasi data dan\r\nfine-tuning mencapai peningkatan akurasi yang signifikan dibandingkan model\r\nbaseline, dengan selisih akurasi mencapai lebih dari 3%. Namun, peningkatan\r\nakurasi ini disertai sedikit tambahan waktu inferensi, meskipun masih dalam batas\r\nyang wajar untuk aplikasi praktis. Secara keseluruhan, MobileNetV3 yang\r\ndioptimalkan mampu memberikan keseimbangan yang baik antara akurasi tinggi\r\ndan efisiensi komputasi, sehingga layak diimplementasikan dalam sistem\r\nklasifikasi spesies bunga berbasis citra.",
      "dir": "disk0\/00\/03\/30\/93",
      "date_type": "published",
      "corp_creators": [
        "Universitas AMIKOM Yogyakarta"
      ],
      "keywords": "obileNetV3, augmentasi data, fine-tuning, klasifikasi spesies bunga,\r\nakurasi, efisiensi komputasi, data augmentation, fine-tuning, flower species\r\nclassification, accuracy, computational efficiency.",
      "date": "2026-05-19",
      "creators": [
        {
          "name": {
            "family": "Yousouf",
            "honourific": null,
            "lineage": null,
            "given": "Honnasan"
          },
          "nim": "22.11.5052"
        }
      ],
      "rev_number": 20,
      "contributors": [
        {
          "name": {
            "given": "Ferian Fauzi",
            "lineage": null,
            "honourific": null,
            "family": "Abdulloh"
          }
        }
      ]
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