Klasifikasi Level Non-Proliferatif Retinopati Diabetik Dengan Ensemble Convolutional Neural Network

Ruvita Faurina(1), Endina Putri Purwandari(2), Mario Tiara Pratama(3), Indra Agustian(4),
(1) Universitas Bengkulu, Indonesia
(2) Universitas Bengkulu, Indonesia
(3) Universitas Bengkulu, 
(4) Universitas Bengkulu, Indonesia

Abstract


Penelitian ini mengusulkan algoritma CNN ensemble classifier untuk klasifikasi level non-proliferatif Retinopati diabetik. Penelitian ini menggunakan metode transfer learning feature-extraction, dan membandingkannya dengan fine-tuning. Pada lapisan pertama lapisan klasifikasi, dibandingkan penggunaan lapisan GAP dan Flatten dengan menggunakan metode dropout. Mode terbaik digunakan sebagai mode final klasifikasi. Arsitektur yang digunakan adalah DenseNet201, InceptionV3 dan MobileNetV2, Masing-masing model diuji dengan optimasi SGD dan ADAM. Keputusan prediksi diambil berdasarkan metode average voting. Hasil pengujian masing-masing arsitektur menunjukkan hasil terbaik adalah fine tuning, GAP, dan optimasi ADAM. Model final fine-tuning DenseNet201, InceptionV3 dan MobileNetV2 dapat mengklasfikasi level retinopati diabetik dengan akurasi pada data uji masing-masing 93%, 94% dan 89%. Sedangkan performa klasifikasi model ensemble untuk masing-masing kelas memiliki akurasi terendah 95,6% dan F1-Score terendah 91.3%.

Kata Kunci: retinopati diabetik, deep learningconvolutional neural networkensemble classifier, DenseNet201,  InceptionV3, MobileNetV2.


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DOI: https://doi.org/10.33369/pseudocode.8.1.1-10

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