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Journal of Advanced Health Informatics Research
Published by Peneliti Teknologi Teknik Indonesia
ISSN : -     EISSN : 29856124     DOI : https://doi.org/10.59247/jahir.v1i1
Core Subject : Health, Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering Medicine & Pharmacology Public Health
Journal of Advanced Health Informatics Research (JAHIR) is a scientific journal that focuses on the application of computer science to the health field. JAHIR is a peer-reviewed open-access journal that is published three times a year (April, August and December). The scientific journal is published by Peneliti Teknologi Teknik Indonesia (PTTI). The JAHIR aims to provide a national and international forum for academics, researchers, and professionals to share their ideas on all topics related to Informatics in Healthcare Research
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol. 2 No. 2 (2024)" : 5 Documents clear
Comparison of Transfer Learning Performance in Lung and Colon Classification with Knowledge Distillation Elsa Wulandari, Annastasya Nabila; Yudhistira , Aimar; Purwono; Sharkawy , Abdel-Nasser
Journal of Advanced Health Informatics Research Vol. 2 No. 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i2.289

Abstract

This research aims to apply the knowledge distillation method to medical image classification, specifically in the case of lung and colon image classification using various transfer learning models. Knowledge distillation allows the transfer of knowledge from a larger model (teacher) to a smaller model (student), which enables more efficient model building without sacrificing accuracy. In this research, the DenseNet169 model is used as the teacher model. The student model uses several alternative transfer learning architectures such as DenseNet121, MobileNet, ResNet50, InceptionV3, and Xception. The data used consists of 25,000 histopathology images that have been processed and divided into training, validation, and test data. Data augmentation was performed to enlarge the dataset from 750 to 25,000 images, which helped improve the performance of the model. Model performance evaluation was performed by measuring the accuracy and loss value of each student model compared to the teacher model. The results showed that the student models generated through the knowledge distillation process performed close to or even exceeded the teacher model in some cases, with the Xception model showing the highest accuracy of 96.95%. In conclusion, knowledge distillation is effective in reducing model complexity without compromising performance, which is particularly beneficial for implementation on resource-constrained devices.
Exploring Blockchain as a Security Framework for IoT in Healthcare: A Systematic Literature Review Lutviana; Iis Setiawan Mangkunegara; Hamzah M. Marhoon
Journal of Advanced Health Informatics Research Vol. 2 No. 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i2.290

Abstract

This systematic literature review explores the use of blockchain technology to improve the security of communication between IoT devices in the healthcare sector. The integration of IoT in healthcare has revolutionized patient data management but introduced security challenges. Blockchain, as a decentralized and immutable ledger, offers a potential solution by providing a secure method of data storage and transmission. This review analyzed 62 relevant studies published in the last five years using the PRISMA methodology. The main contributions of blockchain include improved data security, privacy, and data integrity, with decentralization and cryptographic techniques ensuring patient data remains secure and accessible only to authorized entities. Challenges of blockchain implementation include interoperability, data storage efficiency, and the need for strong cryptographic algorithms. Proposed solutions include the development of a specialized blockchain framework for healthcare, the integration of advanced encryption methods, and the use of distributed ledger technology to manage electronic medical records. Further research is needed to develop more efficient and secure blockchain solutions in healthcare applications, including improved interoperability, encryption algorithms, and real-world case studies. Although challenges remain, blockchain has great potential to improve the communication security of IoT devices in the healthcare sector.
Understanding Transformers: A Comprehensive Review Rahmadhani, Berlina; Purwono, Purwono; Safar Dwi Kurniawan
Journal of Advanced Health Informatics Research Vol. 2 No. 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i2.292

Abstract

Transformers have been recognized as one of the most significant innovations in the development of deep learning technology, with widespread application to Natural Language Processing (NLP), Computer Vision (CV), and multimodal data analysis. The self-attention mechanism, which is at the core of this architecture, is designed to capture global relationships in sequential and spatial data in parallel, enabling more efficient and accurate processing than Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN)-based approaches. Models such as BERT, GPT, and Vision Transformer (ViT) have been used for a variety of tasks, including text classification, translation, object detection, and image segmentation. Although the advantages of this model are significant, the high computing power requirements and reliance on large datasets are major challenges. Efforts to overcome these limitations have been made through the development of lightweight variants, such as the MobileViT and Swin Transformer, which are designed to improve efficiency without sacrificing accuracy. Further research is also directed at the application of transformers for multimodal data and specific domains, such as medical image analysis. With its high flexibility and adaptability, transformers continue to be regarded as a key component in the development of more advanced and far-reaching artificial intelligence.
A Comprehensive Review of Knowledge Distillation for Lightweight Medical Image Segmentation Asmat Burhan; Purwono, Purwono
Journal of Advanced Health Informatics Research Vol. 2 No. 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i2.294

Abstract

Medical image segmentation plays a crucial role in computer-aided diagnosis by enabling precise identification of anatomical and pathological structures. While deep learning models have significantly improved segmentation accuracy, their high computational complexity limits deployment in resource-constrained environments, such as mobile healthcare and edge computing. Knowledge Distillation (KD) has emerged as an effective model compression technique, allowing a lightweight student model to inherit knowledge from a complex teacher model while maintaining high segmentation performance. This review systematically examines key KD techniques, including Response-Based, Feature-Based, and Relation-Based Distillation, and analyzes their advantages and limitations. Major challenges in KD, such as boundary preservation, domain generalization, and computational trade-offs, are explored in the context of lightweight model development. Additionally, emerging trends, including the integration of KD with Transformers, Federated Learning, and Self-Supervised Learning, are discussed to highlight future directions in efficient medical image segmentation. By providing a comprehensive analysis of KD for lightweight segmentation models, this review aims to guide the development of deep learning solutions that balance accuracy, efficiency, and real-world applicability in medical imaging
The Role of AI in Health Research: A Policy Review of Higher Education Foundations Yuris Tri Naili; Efendi Lod Simanjuntak; Anatoly Kostruba
Journal of Advanced Health Informatics Research Vol. 2 No. 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i2.295

Abstract

The integration of Artificial Intelligence in health research has transformed data analysis, predictive modeling, and personalized treatment strategies. However, its rapid adoption presents regulatory, ethical, and institutional challenges, particularly in higher education foundations that oversee health research. This paper examines policies governing AI-driven health research, focusing on regulatory frameworks, ethical guidelines, and institutional policies that shape AI applications in academia. At the global and national levels, regulations such as the EU AI Act and World Health Organization guidelines set standards for AI safety, transparency, and data protection in health research. Despite these frameworks, challenges persist, including data privacy concerns, algorithmic bias, and inconsistent ethical oversight. Ethical frameworks like the Ethical Regulatory Framework for AI emphasize accountability, fairness, and continuous monitoring to ensure responsible AI deployment. Higher education institutions play a crucial role in developing AI governance frameworks that balance innovation with compliance. However, inconsistencies in institutional policies create gaps in regulatory enforcement and ethical standards. Addressing these issues requires harmonized policies, interdisciplinary collaboration, and proactive stakeholder engagement. This paper highlights the role of AI in optimizing research methodologies, funding allocation, and regulatory compliance while discussing emerging challenges and future directions for AI-driven health research governance.

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