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      "thesis_name": "skripsi",
      "abstract": "Katarak adalah penyebab utama kebutaan global yang sebenarnya dapat dicegah melalui\r\ndeteksi dan penanganan dini. Namun, proses skrining secara manual yang dilakukan oleh\r\nspesialis mata dihadapkan pada keterbatasan sumber daya, tingginya biaya pemeriksaan,\r\nserta jangkauan fasilitas kesehatan yang tidak merata di berbagai daerah. Keterbatasan\r\nakses terhadap deteksi dini ini menghadirkan tantangan diagnostik yang serius, karena\r\nmenghambat penanganan tepat waktu dan secara langsung berdampak pada peningkatan\r\nangka kebutaan permanen di masyarakat, terutama pada negara-negara berkembang. Oleh\r\nkarena itu, diperlukan sebuah solusi inovatif berbasis teknologi untuk mengotomatisasi\r\nproses analisis citra medis secara cepat, akurat, dan dapat diakses dengan mudah oleh\r\nberbagai kalangan. \r\nPenelitian ini mengusulkan solusi otomatisasi menggunakan pendekatan Pembelajaran\r\nMendalam (Deep Learning), secara khusus menerapkan teknik Transfer Learning dengan\r\nmemanfaatkan arsitektur Convolutional Neural Network (CNN) ringan yaitu\r\nMobileNetV2. Alur penyelesaian masalah diawali dengan pengumpulan 3.709 citra mata\r\ndari dataset publik Kaggle yang dibagi menjadi tiga kelas kondisi: katarak matur, katarak\r\nimatur, dan mata normal. Data tersebut kemudian melalui tahap prapemrosesan,\r\naugmentasi, pembagian dataset (rasio 70:15:15), dan penyesuaian bobot kelas untuk\r\nmenangani ketidakseimbangan data. Proses pelatihan model dioptimalkan secara dinamis\r\nmenggunakan kombinasi tiga fungsi kontrol (callbacks), yakni ModelCheckpoint untuk\r\nmenyimpan bobot terbaik, ReduceLROnPlateau untuk menyesuaikan nilai learning rate\r\nsaat konvergensi stagnan, dan EarlyStopping untuk mencegah indikasi overfitting.\r\nEvaluasi kinerja model diukur secara komprehensif menggunakan metrik akurasi,\r\nprecision, recall, F1-score, dan confusion matrix pada sekumpulan data uji independen. \r\nHasil evaluasi menunjukkan bahwa model yang dibangun berhasil mencapai tingkat\r\nakurasi pengujian yang sangat tinggi, yaitu sebesar 99%. Analisis metrik komprehensif\r\nmembuktikan performa yang seimbang, di mana secara rata-rata (macro average) model\r\nmencatatkan nilai 0.99 untuk precision, recall, dan F1-score. Penerapan callbacks juga\r\nterbukti mengefisienkan waktu komputasi pelatihan secara substansial menjadi 1.171\r\ndetik. Kontribusi utama dari penelitian ini adalah terciptanya model klasifikasi yang\r\nsangat ringan, efisien, dan andal dalam meminimalkan kesalahan diagnosis medis. Hasil\r\npenelitian ini dapat dimanfaatkan secara langsung oleh tenaga medis sebagai alat bantu\r\nskrining untuk mempercepat proses diagnosa, serta oleh masyarakat umum untuk\r\nmengakses pemeriksaan awal katarak secara mandiri. Untuk penelitian lebih lanjut,\r\ndirekomendasikan pengintegrasian metode Explainable AI (XAI) seperti Grad-CAM\r\nguna meningkatkan interpretabilitas medis, serta pengembangan sistem menuju deteksi\r\nobjek (object detection) agar mampu melokalisasi area kekeruhan lensa secara spesifik.",
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      "title": "OPTIMALISASI KLASIFIKASI CITRA MEDIS KATARAK\r\nMENGGUNAKAN ARSITEKTUR MOBILENETV2 DENGAN\r\nPENERAPAN CALLBACKS",
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