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All Journal Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA
Putu Hendra Suputra
Universitas Pendidikan Ganesha, Singaraja
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Pengembangan Media Pembelajaran Neuroanatomi Berbasis WebXR (Website Extended Reality) dengan Pendekatan Aksesibilitas dan Optimalisasi Gede Bramanda; I Gede Partha Sindu; Putu Hendra Suputra
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9954

Abstract

This study aims to develop and evaluate a WebXR (Website Extended Reality)-based learning media specifically designed for medical students at Universitas Pendidikan Ganesha. Neuroanatomy learning is a crucial yet challenging component of medical education, often difficult to comprehend due to limited learning media, the scarcity of cadavers, and the abstract nature of the SOBOTA anatomical atlas. The primary contribution of this research lies in the integration of an affordable and easily accessible web-based immersive platform, which simultaneously resolves technical constraints such as motion sickness and stuttering on standalone devices. Behind a well-structured and designed foundation, this media operates on a server-based infrastructure and runs on HTML5 technology architecture to deliver flexible, real-time data accessibility directly through the browser. Technical optimization was implemented in the form of FPS stabilization (FPS Locking) and dynamic resolution scaling to maximize the computing capacity of the Snapdragon XR2 chipset on Meta Quest 2. This application was built using the Multimedia Development Life Cycle (MDLC) framework. The assessment results show that the application has very high validity from both material and media experts, achieving an overall score of 1.00 based on the Gregory matrix. Furthermore, user experience testing using the User Experience Questionnaire (UEQ) with medical students yielded an "Excellent" rating across all dimensions, with the Stimulation dimension receiving the highest score (2.46), placing this application within the top 10% of products globally. The findings indicate that the developed WebXR media effectively offers immersive appeal and can serve as a high-quality, interactive, and cross-platform alternative practical tool for neuroanatomy introduction.
Klasifikasi Jenis Kelamin Berbasis Citra Mata Menggunakan Vision Transformer ViT dengan Strategi Discriminative Fine-Tuning Gde Made Hanura; Putu Hendra Suputra
Journal of Computer System and Informatics (JoSYC) Vol 7 No 3 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i3.9778

Abstract

Face-based biometric identification systems have significant limitations when a subject’s face is covered, whether due to mask usage after the COVID-19 pandemic or face veils for cultural and religious reasons. This creates real security gaps, as evidenced by the gender-disguise infiltration incident at Masjid Jannatul Firdaus in Makassar. In such situations, the eyes remain the only consistently exposed biometric feature. This study proposes the application of Vision Transformer (ViT-B/16) pretrained on ImageNet-21K with a progressive fine-tuning strategy based on the discriminative learning rate principle to classify gender from eye images. The Female and Male Eyes dataset from Kaggle consists of 11,525 eye images divided into training (64%), validation (16%), and testing (20%) sets. Experiments were conducted in two series: Series B tested variations in the number of unfrozen transformer blocks (0–6), and Series C tested discriminative learning rate ratios between the classifier and encoder (5:1, 10:1, 3:1). The optimal configuration with 6 unfrozen blocks and a 3:1 ratio achieved 95.70% accuracy, 97.67% precision, 92.69% recall, and 0.9569 weighted F1-score, surpassing MobileNet (93.90%) and K-Nearest Neighbor (68.81%). These results indicate that ViT with discriminative fine-tuning is effective for gender classification from eye images and has potential for biometric security applications.
Co-Authors Gde Made Hanura Gede Bramanda I Gede Partha Sindu