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Penelitian ini bertujuan\r\nmembandingkan kinerja dua arsitektur Convolutional Neural Network (CNN), yaitu\r\nSqueezeNet dan ShuffleNet, dalam klasifikasi penyakit mata berbasis citra retina.\r\nDataset yang digunakan berasal dari Kaggle dan terdiri dari empat kelas, yaitu\r\ncataract, diabetic retinopathy, glaucoma, dan normal. Penelitian menerapkan\r\ntransfer learning dengan dua strategi pelatihan, yaitu fixed feature extraction dan\r\npartial fine-tuning. Evaluasi dilakukan menggunakan metrik akurasi, presisi, recall,\r\nF1-score, efisiensi komputasi, serta pendekatan Explainable Artificial Intelligence\r\n(XAI) melalui Grad-CAM.\r\nHasil penelitian menunjukkan bahwa strategi partial fine-tuning\r\nmemberikan performa yang lebih baik dibandingkan fixed feature extraction pada\r\nkedua model. ShuffleNet dengan partial fine-tuning memperoleh akurasi tertinggi\r\nsebesar 91%. Dari sisi efisiensi komputasi, SqueezeNet memiliki ukuran model dan\r\njumlah parameter yang lebih kecil dan waktu pelatihan cepat dibandingkan\r\nShuffleNet. Berdasarkan visualisasi Grad-CAM, ShuffleNet menunjukkan fokus\r\nperhatian yang lebih terarah pada area retina yang merepresentasikan gejala\r\npenyakit. Secara keseluruhan, ShuffleNet dengan partial fine-tuning merupakan\r\nmodel paling optimal karena mampu memberikan performa klasifikasi dan\r\ninterpretasi visual yang lebih baik, meskipun membutuhkan sumber daya\r\nkomputasi yang lebih besar dibandingkan SqueezeNet.", "date_type": "published", "subjects": [ "000.000.000A" ], "eprint_status": "archive", "userid": 12, "status_changed": "2026-10-01 03:40:59", "metadata_visibility": "show", "institution": "Universitas AMIKOM Yogyakarta", "divisions": [ "if" ] }