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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Prasetya, I Kadek Diksa Bayu
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Computer Science & IT Decision Sciences, Operations Research & Management

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Klasifikasi Tingkat Keparahan Penyakit Diabetic Retinopathy menggunakan Convolutional Neural Network Ginantra, Ni Luh Wiwik Sri Rahayu; Hendrawati, Theresia; Prasetya, I Kadek Diksa Bayu
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): Juli 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7432

Abstract

Diabetic Retinopathy is an eye condition in Diabetes sufferers that causes damage to the retina, which can result in permanent blindness if not treated properly. The initial stage of this disease is the widening of the blood vessels in the eye which, if left untreated, can cause the formation of new blood vessels which can cover the retina of the eye, thereby increasing the risk of vision loss. There are several classes of Diabetic Retinopathy disease; to determine the class you can use the Deep Learning method which can model various data such as images. The classification process is carried out by training a Convolutional Neural Network model on a disease image dataset taken from the Kaggel repository with a total of 5 classes. This research uses a Fine Tuning strategy and the EfficientNetB1 model to determine the performance of the CNN model in the Diabetic Retinopathy Classification process. Based on training results, the EfficientNetB1 model produces 92.51% accuracy in detecting Diabetic Retinopathy. These results show that the model can provide optimal results in the dataset training process.
Co-Authors Hendrawati, Theresia Ni Luh Wiwik Sri Rahayu Ginantra