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      "title": "PENGEMBANGAN ARSITEKTUR HIBRIDA EFFICIENTNETVIT\r\nDENGAN\r\nEXPLAINABLE\r\nAI\r\n(XAI)\r\nUNTUK\r\nDETEKSI\r\n\r\nPENYAKIT\r\nTANAMAN\r\nPISANG",
      "keywords": "Penyakit Daun Pisang, Arsitektur Hibrida, EfficientNetV2, Vision\r\nTransformer, Grad-CAM, Banana Leaf Disease, Hybrid Architecture.",
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      "date": "2026-06-18",
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            "family": "Hasri",
            "honourific": null,
            "given": "Rasyif Ibnu"
          },
          "nim": "22.11.4710"
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      "rev_number": 15,
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      "type": "thesis",
      "thesis_name": "skripsi",
      "abstract": "Penyakit pada tanaman pisang seperti Sigatoka, Cordana, dan Panama\r\nmerupakan ancaman serius yang berdampak pada terganggunya ketahanan pangan.\r\nDiagnosis manual dinilai lambat, sehingga dibutuhkan sistem deteksi otomatis yang\r\ntangguh. Sayangnya, penggunaan model deep learning tunggal menghadapi\r\nketerbatasan arsitektural. Convolutional Neural Network (CNN) kesulitan\r\nmenangkap konteks spasial jarak jauh, sedangkan Vision Transformer (ViT) rentan\r\nmengalami overfitting pada dataset berskala kecil. \r\nUntuk menyelesaikan masalah tersebut, penelitian ini merancang arsitektur\r\nhibrida yang mengintegrasikan EfficientNetV2 (pengekstraksi fitur lokal) dan ViT\r\n(pengekstraksi konteks global). Metode ini menggunakan 4.675 citra sekunder\r\nuntuk proses permodelan, serta 40 citra lapangan primer untuk pengujian validasi\r\neksternal (mengukur pergeseran domain). Selain itu, teknik Explainable AI berbasis\r\nGrad-CAM diterapkan untuk memvisualisasikan landasan pengambilan keputusan\r\ndan mengatasi sifat kotak hitam. \r\nHasil pengujian internal menunjukkan EfficientNetV2 mencapai akurasi\r\ntertinggi 99,29%, diikuti ViT (98,58%), dan Hibrida (97,88%). Namun, pada\r\npengujian eksternal di lapangan, ViT turun drastis menjadi 62,50%. Sementara itu,\r\nEfficientNetV2 (82,50%) dan Hibrida terbukti stabil di angka 80,00%. Analisis\r\nGrad-CAM memvalidasi bahwa model hibrida mengevaluasi geometri lesi secara\r\nholistik melalui mekanisme self-attention, sehingga mampu mengatasi kelemahan\r\nspesifik CNN yang hanya mengunci fitur tepi lesi secara parsial dan rentan salah\r\nklasifikasi pada kasus dengan kemiripan tekstur antar-kelas penyakit. Penelitian ini\r\nberkontribusi menghasilkan sistem deteksi andal dan transparan bagi praktisi\r\npertanian. Untuk penelitian lanjutan, direkomendasikan menerapkan segmentasi\r\nlatar belakang, pengayaan augmentasi data lapangan, kompresi arsitektur seluler,\r\nserta restrukturisasi lapisan penyandian posisi.",
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            "lineage": null,
            "given": "Ferian Fauzi",
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