Penerapan Convolutional Neural Network Untuk Klasifikasi Tingkat Keparahan Retinopati Diabetik Pada Penderita Diabetes Melitus

Fithra Hayati Syahrul; Priyo Sidik Sasongko

doi:10.14710/jmasif.13.1.42354

DOI: https://doi.org/10.14710/jmasif.13.1.42354

Penerapan Convolutional Neural Network Untuk Klasifikasi Tingkat Keparahan Retinopati Diabetik Pada Penderita Diabetes Melitus

Fithra Hayati Syahrul, Priyo Sidik Sasongko

Departemen Ilmu Komputer/Informatika, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275, Indonesia

Received: 4 Oct 2021; Revised: 1 Nov 2021; Accepted: 2 Nov 2021; Available online: 27 May 2022; Published: 27 May 2022.

Editor(s): Fajar Nugroho

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

Retinopati Diabetik adalah penyakit yang dapat menganggu pembuluh darah retina yang menjadi penyebab kebutaan bagi penderita Diabetes Melitus. Jika penyakit ini terlambat ditangani maka penderita dapat mengalami kebutaan. Perawatan dan pemeriksaan yang tepat dapat membantu mencegah meningkatnya keparahan Retinopati Diabetik. Pemeriksaan secara manual oleh dokter mata dalam mendiagnosis penyakit ini membutuhkan waktu yang relatif lama, sehingga diperlukan sistem untuk mengklasifikasikan tingkat keparahan Retinopati Diabetik. Sistem yang dirancang pada penelitian ini menggunakan metode Convolutional Neural Netwok untuk klasifikasi tingkat keparahan Retinopati Diabetik. Tingkat keparahan Retinopati Diabetik dibagi menjadi 5 kelas yaitu NO DR, Mild, Moderate, Severe, dan Proliferative DR. Penelitian Penerapan Convolutional Neural Netwok untuk Klasifikasi Tingkat Keparahan Retinopati Diabetik pada Penderita Diabetes Melitus menggunakan citra berukuran 64 x 64 x 3 dengan channel RGB. Tahap pra-pengolahan citra yang dilakukan adalah pengubahan ukuran citra. Arsitektur CNN yang digunakan terdiri dari 5 blok dimana masing-masing blok berisi batch normalization layer, convolution layer, max pooling layer menggunakan parameter learning rate 0.0005. Hasil evaluasi model 652 data uji menunjukkan akurasi terbaik sebesar 91.10%.

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Keywords: Retina; Retinopati diabetik; Convolutional Neural Network

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Section: Research Article

Language : ID

In Vol 13, No 1 (2022): JURNAL MASYARAKAT INFORMATIKA

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