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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Yupa: Historical Studies Journal InComTech: Jurnal Telekomunikasi dan Komputer JISA (Jurnal Informatika dan Sains) Jurnal Teknologi Informatika dan Komputer Academic Journal of Computer Science Research (AJCSR) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Lentera Pengabdian SAINTEK
Donny Maulana
Universitas Pelita Bangsa
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Eye Disease Detection and Classification Optimization Using EfficientNet-B5 with Emphasis on Data Augmentation and Fine-Tuning Anggi Muhammad Rifai; Muhammad Fatchan; Ahmad Turmudi Zy; Donny Maulana; Sufajar Butsianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6519

Abstract

Eye diseases such as glaucoma, cataract, and diabetic retinopathy pose significant global health challenges, underscoring the need for accurate and efficient diagnostic systems. This study employed the EfficientNet-B5 model to enhance the detection and classification of eye diseases by incorporating advanced data augmentation and fine-tuning techniques. The research utilizes the Ocular Disease Intelligent Recognition (ODIR) dataset, consisting of 4,217 fundus images categorized into four classes: normal, glaucoma, cataract, and diabetic retinopathy. The methodology comprises three phases: baseline model training, model training with data augmentation, and fine-tuning. The baseline model achieved an accuracy of 60.43%, which improved to 63.03% with data augmentation—an increase of 2.6 percentage points. Fine-tuning further elevated the accuracy to 93.23%, representing a notable improvement of 33.8 percentage points over the baseline. Model performance was evaluated using standard classification metrics including accuracy, precision, recall, and F1-score. These findings demonstrate the technical efficacy of combining augmentation and fine-tuning to enhance model generalization. The proposed approach offers a robust framework for developing dependable AI-driven diagnostic tools to support early detection and facilitate informed clinical decision-making.
Co-Authors Agung Nugroho Agung Nugroho Agus Suwarno, Agus Ahmad Turmudi Zy Ahmad Turmuzdy ZY Alfatan Dzulatkha2 Andri Firmansyah Anggi Muhammad Rifai Asep Saepudin Butsianto, Sufajar Edri Fauzan Emi Lia Nurjanah Gatot Tri Pranoto Hariyono Hariyono Hasim Budi Jatmiko Ikhsanudin ikhsanudin Indah Wahyu Puji Utami, Indah Wahyu Irfan Afriantoro Ismamudi, Ismamudi Lutfi Adji Ardiansyah Maryatul Kiptiyah Maulana Sofyan Muhammad Fatchan Muhtajuddin Danny Nanang Tedi Nanang Tedi Kurniadi Nawangsih, Ismasari Novita Sari Raden Rangga Bramantya Putra Rensi Suryanti Rezayadi Yahya Riady, Sasmitoh Rahmad Rudi Rahmanto2 Shinta Mawarni Slamet Nur Ihsan Suherman Suherman Taufik Hidayat Ucok Darmanto Soer Vivi Mustikaningtyas Wildan Trio Munawarudin Wiyanto - Yoga Religia Yusup Solikhun