Development of Augmented Reality-Based Anatomy Learning Media (ARANOMI) for Vocational Health Students

Authors

  • Yoga Sahria Yogyakarta State University
  • Edwina Nur Utami Al-Islam Yogyakarta College of Health Sciences

DOI:

https://doi.org/10.57152/malcom.v6i1.2374

Keywords:

Anatomy and Physiology, ARANOMI, Augmented reality, Healthy Lifestyle, Nursing Education

Abstract

This study aims to develop and implement the Augmented Reality Anatomy and Physiology (ARANOMI) application as an innovative learning medium in nursing education, specifically for understanding anatomy and physiology and promoting the practice of clean and healthy living (PHBS). The application is designed as a mobile-based platform supported by WebAR and contains content such as 3D organ models, explanations of physiological functions, and integrated PHBS education. The research employed a Research and Development (R&D) approach, consisting of stages of design, development, and implementation. The implementation results among nursing students showed a positive impact on their understanding and learning motivation. Based on the evaluation, 87% of students reported that learning became more engaging, while 82% stated they felt more motivated to study anatomy and physiology. In addition, most students actively participated in group discussions and independent exploration through the application. Student enthusiasm indicates that ARANOMI provides a more realistic, interactive, and contextual learning experience. However, several technical challenges were identified, including long loading times for 3D models on low-specification devices and dependence on a stable internet connection. This study concludes that the ARANOMI application effectively supports the anatomy and physiology learning process using augmented reality technology, enhances student engagement.

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Published

2026-01-15

How to Cite

Sahria, Y., & Utami, E. N. (2026). Development of Augmented Reality-Based Anatomy Learning Media (ARANOMI) for Vocational Health Students. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 6(1), 62-72. https://doi.org/10.57152/malcom.v6i1.2374