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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITAKU
Anggi Muhammad Rifai
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Computer Science & IT Engineering Other

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Perancangan Model 3D sebagai Media Promosi untuk Perumahan Ciromani Permai Anggi Muhammad Rifai; Ikhwan Ma’ruf Baharuddin
Jurnal Ilmiah Teknologi Informatika Vol 2 No 1 (2024): JITAKU: Jurnal Ilmiah Teknologi Informatika UNCP
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/jitaku.v2i1.87

Abstract

Perumahan Ciromani Permai masih menggunakan brosur dan informasi mulut ke mulut tanpa gambaran jelas tentang model perumahan. Dengan memanfaatkan teknologi 3D, media promosi ini diharapkan dapat memberikan visualisasi nyata mengenai tipe rumah yang tersedia. Penelitian ini menggunakan metode Research and Development (R&D) dengan pendekatan Multimedia Development Life Cycle (MDLC) yang mencakup tahapan: Konsep, Desain, Pengumpulan Material, Perakitan, Pengujian, dan Distribusi. Aplikasi Model 3D Media Promosi terdiri dari tampilan seperti halaman loading, menu utama, profil pembuat, profil perusahaan, profil perumahan, model 3D, dan info perumahan. Hasil pengujian black box menunjukkan aplikasi berfungsi dengan baik dan memenuhi harapan.
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 Ahmad Turmudi Zy Butsianto, Sufajar Donny Maulana Ikhwan Ma’ruf Baharuddin Muhammad Fatchan