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All Journal JISKa (Jurnal Informatika Sunan Kalijaga)
Laudza Atsila Prasetyo
Universitas Islam Negeri Maulana Malik Ibrahim
Computer Science & IT Electrical & Electronics Engineering Library & Information Science

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Klasifikasi Penyakit Mata Berdasarkan Citra Fundus Menggunakan Metode Multi-Layer Perceptron Laudza Atsila Prasetyo; Okta Qomaruddin Aziz; Tri Mukti Lestari
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.6022

Abstract

This research aims to evaluate the performance of the Multi-Layer Perceptron (MLP) for classifying eye diseases from fundus images in the ODIR dataset, which comprises four classes: Normal, Diabetic, Glaucoma, and Cataract. The methodology includes feature extraction using GLCM and Gabor, data pre-processing through cleaning, augmentation, and undersampling, and testing 16 model scenarios with variations in the number of hidden layers (2 and 3) and neuron configurations. The results show that data balance and dataset size are the most influential factors affecting model performance, with the best results achieved through the combination of undersampling and augmentation. The optimal architecture was obtained with the 64–32-neuron configuration, yielding a mean accuracy of 73.06%. Overall, this study concludes that combining a balanced dataset with a proportional MLP architecture significantly improves the model’s ability to classify eye diseases from fundus images.
Co-Authors Okta Qomaruddin Aziz Tri Mukti Lestari