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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 36 Documents
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Coffee Culture and Mental Well-being: A Comparative Study of Modern and Traditional Coffeeshops in Al Qassim Maspul, Kurniawan Arif
Journal of Advanced Health Informatics Research Vol. 2 No. 1 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i1.95

Abstract

This study looks into the effects of modern and traditional coffeeshops on the mental health of customers in the Saudi Arabian province of Al Qassim, with a focus on the cities of Buraydah and Unaizah. The proliferation of diverse coffeeshop kinds has resulted in the emergence of each giving a distinct experience and atmosphere. Understanding the impact of different coffee shop types on mental well-being is critical for the creation of long-lasting and prosperous coffee communities in Al Qassim. The study addresses five major concerns: the impact of coffee shops on consumers' sense of autonomy and empowerment, the impact of environmental stimuli on psychological well-being, the role of quality and satisfaction in shaping coffee shop experiences, strategies for bridging the gap between modern and traditional coffeeshops. To gain insights, a thorough research technique was used, which included a qualitative literature review, talks, and observations. The findings emphasize the considerable impact of modern and traditional coffeeshops on mental health, underlining the necessity of collaboration and sustainability in cultivating an inclusive coffee culture that improves the well-being of the Al Qassim society.
Smart Contracts for Data Sharing in Drug Development a Systematic Review of Security and Transparent Measurement Elsa Wulandari, Annastasya Nabila; Purwono
Journal of Advanced Health Informatics Research Vol. 2 No. 1 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i1.288

Abstract

This systematic literature review explores the role of smart contracts in improving data sharing for drug development, with an emphasis on security and transparency. Using blockchain technology, smart contracts offer a decentralized tracking mechanism for pharmaceutical supply chains, addressing challenges related to drug authentication and supply chain optimization. The review examined 52 studies using the PRISMA methodology, highlighting the automation of data exchange, reduced reliance on external parties, and acceleration of operational processes. Advanced encryption and strict access controls in smart contracts strengthen data security, ensuring patient confidentiality and compliance with medical data regulations. Despite technical and regulatory barriers, smart contracts promise significant improvements in operational efficiency, transparency, and collaboration among stakeholders in drug development. This study emphasizes the need for standardized protocols, further empirical research, and strategic implementation to fully leverage the potential of smart contracts in the pharmaceutical industry. Integration of these technologies can accelerate clinical trials and improve data reliability, thereby enhancing the safety and effectiveness of the drug development process.
Predicting internal diseases in humans using machine learning: A systematic literature review Al-Hakim, Rosyid; Prokopchuk, Yurii
Journal of Advanced Health Informatics Research Vol. 2 No. 1 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i1.195

Abstract

Human health is the main focus of clinical medicine, especially in understanding internal diseases involving the body's organs. Identifying and predicting disease at an early stage is essential to prevent the development of more severe disease. These challenges encourage using the latest technologies, especially machine learning techniques. This technology is used to ensure accurate disease predictions. The results of the research identified various types of internal diseases, including heart, kidney, lung and liver cancer, and highlighted the associated symptoms and risk factors. Several algorithms are used to classify internal diseases, including the classification of heart disease. The logistic regression algorithm is the most efficient, with accuracy results of 88.52%. Meanwhile, CHIRP kidney disease classification provides the most efficient results with an accuracy of 99.75%. MobileLungNetV2 has an accuracy of 96.97% for lung disease classification, and classification for liver disease produces the highest accuracy in logistic regression at 72.50%. Machine learning in disease prediction significantly contributes, especially in increasing accuracy and efficiency in diagnosis and risk prediction. Despite significant progress, challenges such as dataset size, data quality, and model validation need to be addressed to maximise the potential of this technology in clinical practice.
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.
The Impact of Robotic Technology on Nursing Care: A Systematic Review Sylvia Rosa Enjelina Bastian; Muflih Muflih; Deden Iwan Setiawan
Journal of Advanced Health Informatics Research Vol. 2 No. 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i3.240

Abstract

The rapid development of technology has given birth to various sophisticated innovations, one of which is robot technology. In the context of today's evolving nursing world, attention to nurses' ability to deal with technological developments, including the use of robots, is becoming increasingly important to consider the steps that need to be taken in order to achieve and maintain patient health with the help of robots as intelligent tools. The aim of this study was to thoroughly assess the impact of the use of robotic technology in nursing care through a systematic review of relevant literature. This type of research is a systematic literature review. The sample in this study was 17 articles using the prism diagram method. Article retrieval was carried out using the Sage Journal search engine, PubMed, ProQuest, Google Scholar.  Based on the results obtained positive and negative impacts of the use of robotic technology in nursing services. Positive impacts include aspects of mobilization, psychology, independent needs, nutritional fulfillment, safety, logistical needs, time management. However, the main challenges include economic, psychological, management, collaboration, social values. These studies meet the five assessments of service quality; Physical appearance, reliability, responsiveness, assurance, empathy. Apart from these five assessments, there are economic, management, collaboration challenges that need to be considered
A Short Review on Harnessing Bioinformatics for Food Safety: Computational Approaches to Detecting Foodborne Pathogens Syaiful Khoiri; Victor Davy Moussango
Journal of Advanced Health Informatics Research Vol. 2 No. 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i3.296

Abstract

Foodborne diseases remain a significant global public health concern, affecting millions annually and causing substantial economic losses. Traditional microbiological methods for pathogen detection, such as culture-based identification and polymerase chain reaction, are often time-consuming and lack sensitivity. The integration of bioinformatics and high-throughput sequencing technologies, including next-generation sequencing and metagenomics, has revolutionized foodborne pathogen detection by enabling rapid, accurate, and culture-independent identification. Machine learning and artificial intelligence further enhance food safety monitoring through predictive modeling and risk assessment, facilitating early outbreak detection and improved contamination control. Whole genome sequencing has emerged as a gold standard for public health surveillance, allowing for precise pathogen characterization and antimicrobial resistance tracking. Data-sharing networks, such as GenomeTrakr and PulseNet, have strengthened global collaboration, enhancing real-time pathogen monitoring. However, challenges persist in data integration, technical expertise, and infrastructure development, which hinder the widespread adoption of these technologies. Addressing these barriers requires standardized protocols, AI-driven predictive models, and interdisciplinary collaboration between public health, industry, and academia. As bioinformatics continues to evolve, its role in pathogen surveillance, outbreak prevention, and food safety management will become increasingly vital. Advancements in bioinformatics tools and AI-driven approaches will ensure a more efficient, data-driven, and globally coordinated response to foodborne disease threats

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