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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika Jurnal Ilmiah KOMPUTASI Jurnal Komputer Terapan Jusikom : Jurnal Sistem Komputer Musirawas SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknologi Informasi MURA Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal KOMPUTIKA - Jurnal Sistem Komputer Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Computer Networks, Architecture and High Performance Computing Bulletin of Computer Science Research Brilliance: Research of Artificial Intelligence Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Klik - Jurnal Ilmu Komputer Jurnal Teknologi dan Manajemen Industri Terapan JEECS (Journal of Electrical Engineering and Computer Sciences) MDP Student Conference Jurnal Penelitian Teknologi Informasi dan Sains Jurnal Pengabdian Masyarakat Nian Tana
Nur Rachmat
Universitas Multi Data Palembang, Kota Palembang
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Journal : bulletin of computer science research

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Perbandingan Kinerja Arsitektur MobileneTV2 dan MobileneTV3 Dalam Klasifikasi Penyakit Retina pada Citra Optical Coherence Tomography (OCT) Menggunakan Optimizer AdamW dan SGD Ricko Andreas Kartono; Nur Rachmat
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.915

Abstract

Retinal diseases are serious visual disorders that can lead to decreased visual function and even blindness. The diagnosis of retinal diseases is generally still performed manually by medical professionals through the examination of Optical Coherence Tomography (OCT) images, a process that requires considerable time, high precision, and is prone to diagnostic errors. Previous studies have mostly employed larger and more complex CNN architectures, with optimization limited to a few commonly used optimizers. This study aims to develop an automatic retinal disease classification model using Convolutional Neural Network (CNN) methods by leveraging the lightweight and efficient MobileNetV2 and MobileNetV3 architectures, enabling faster applications that can be deployed on resource-constrained devices. The architectures evaluated include MobileNetV2, MobileNetV3-Large, and MobileNetV3-Small, along with a comparison of two optimizers, namely AdamW and Stochastic Gradient Descent (SGD). The dataset used consists of 4,000 OCT images divided into four classes: Normal, Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen. The training process was conducted using a transfer learning approach, and model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results indicate that the combination of the MobileNetV2 architecture with a batch size of 16 and either the AdamW or SGD optimizer achieved the best performance, reaching an accuracy of 85.75%, which is the highest among all tested configurations. These findings highlight the strong potential of lightweight architectures to be developed into fast, accurate, and field-deployable retinal disease diagnostic applications on mobile devices using deep learning.
Evaluasi Efektifitas Optimizer Adam dan SGD pada Klasifikasi Citra Dermoskopi dengan MobileNetV4 Ahmad Naufal; Nur Rachmat
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1165

Abstract

Skin disease is one of the most common health problems and requires fast and accurate diagnosis. The limited availability of dermatology specialists and the high subjectivity of conventional diagnosis have driven the development of artificial intelligence-based automatic classification systems. This study aims to compare the performance of the Adam and Stochastic Gradient Descent (SGD) optimizers on the MobileNetV4 architecture for classifying eight classes of skin diseases using the ISIC 2019 dataset. The dataset consists of 23,257 valid dermoscopic images after preprocessing, which includes duplicate image removal, hair artifact elimination using the blackhat morphology method, and an asymmetric sampling strategy in which majority classes were capped at a maximum of 2,000 images while minority classes were augmented to reach the target count, in order to address extreme class imbalance with a ratio of up to 53:1. The model was trained using a three-phase training strategy with gradual unfreezing of the MobileNetV4 backbone initialized with pretrained ImageNet weights. All training configurations were made identical for both optimizers except for the optimization algorithm and learning rate, ensuring a fair comparison. Evaluation results on the test set show that the Adam optimizer achieved an accuracy of 71.07% with a macro F1-score of 0.72, while SGD achieved an accuracy of 58.06% with a macro F1-score of 0.57. Adam outperformed SGD across all eight skin disease classes. The performance difference of 13.01% indicates that Adam's adaptive learning rate mechanism is more effective for dermoscopic datasets with imbalanced class distributions compared to SGD. Nevertheless, it should be noted that Adam requires greater computational memory than SGD due to the storage of first and second moment estimates per parameter, and therefore the computational efficiency trade-off should be considered when deploying the model on resource-constrained devices. This study provides empirical contribution in selecting the optimal optimizer for skin lesion classification based on lightweight architectures.
Co-Authors Adhytio Mahendra Ahmad Naufal Andrian Wijaya Ardi al Ghifari Ardi Bobby Jaya Saputra Calvin Oliver Saputra Davin Hansel Winata Devella, Siska Dicky Ryanto Fernandes Elizabeth Fiona Hartono Farisi, Ahmad Fionna Caroline Hakiki, Muhammad Anugrah Indrawan Indrawan Intan Sanu Izha Mahendra Johannes Petrus Michael Lorens Molavi Arman Monica Rizki Ayustin Muhammad Alfa Rizi Muhammad Ezar Al Rivan Muhammad Naufal Anugrah Ricko Andreas Kartono Riski Ariza, Agung Russel Wijaya Saputra, Muhammad Redho Ubaidillah Ubaidillah Vannes Wijaya Vincent Wahyu Sudrajat Wesley Maximilliano Wibowo, Suluh Arif Yanto, Vicky Yohannes, Yohannes