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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. 1 (2024)" : 5 Documents clear
Enhancing Nursing Students' Long-term Retention and Engagement in Medical Terminology through Mnemonic-Enhanced Multimedia Mobile Learning Barlian Kristanto; Thanee Glomjai; Diannike Putri
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.v2i1.178

Abstract

Medical terminology poses a significant learning obstacle for numerous nursing students who rely heavily on textbook definitions. However, these definitions often lack the necessary visual context to facilitate lasting comprehension. In order to enhance literacy skills crucial for safe nursing practice, it is important to explore innovative approaches. This study aimed to evaluate the effectiveness of the Picmonic application, which utilizes audiovisual narratives, in improving retention and engagement compared to traditional studying methods. A parallel group randomized controlled trial compared two learning methods among first-year nursing undergraduates at an urban university. Participants were assigned to either a textbook self-directed learning group (n=62) or an equivalent Picmonic content group (n=60), which used mnemonic visual flashcards and quizzes. Assessments were conducted at 5-, 10-, and 15-week intervals, with additional delayed testing at 1- and 3-months to examine knowledge acquisition and persistence, and to assess the effectiveness of the learning system. Students who used Picmonic had higher average test scores compared to the control group at various measurement points (p < .001). The differences in group means increased over longer intervals, suggesting that the use of multimedia in Picmonic helped with long-term recall. Picmonic users also expressed high satisfaction, voluntarily used the system, and provided positive feedback in focus groups, indicating a preference for the mnemonic-enhanced methodology (p < .001). Multimedia mnemonic educational systems, such as Picmonic, enhance medical terminology retention and engagement of nursing students compared to traditional strategies. This finding has significant implications for instructional design and clinical preparation.
Implementation of Intelligent Pneumonia Detection Model, Using Convolutional Neural Network (CNN) and InceptionV4 Transfer Learning Fine Tuning Anggit Wirasto; Purwono, Purwono; Muhammad Baballe Ahmad
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.v2i1.180

Abstract

In Pneumonia is a type of contagious lung infection that has caused many human deaths in the form of inflammation of the alveoli. Based on WHO data, pneumonia is a type of acute infection that has caused more than 450 million cases and 4 million deaths each year. Covid-19 is one of the global pandemics that triggered many pneumonia incidents. Chest X-rays (CXR) are an important part of patient care. Radiologists can use CXR features to determine the type of pneumonia and the underlying pathogenesis. Machine learning and deep learning technologies are used to automatically detect various human diseases, thus ensuring smart healthcare. CXR features are more suitable to be analyzed by convolutional neural network (CNN). This algorithm is one of the typical deep learning architectures that has strong characteristics that are widely applied in the healthcare field. This study aims to develop a deep learning-based paradigm to distinguish Covid-19 patients from healthy and normal individuals by analyzing the presence of pneumonia disease symptoms on the CXR. This research provides an approach to the use of InceptionV4 transfer learning type in performing classification on CXR images. There are three main approaches carried out, namely making a standard CNN model, optimizing transfer learning xceptiion and fine tuning. The performance metrics results show a recall value close to 100% with a model accuracy value of 88%. Achieving a high enough recall value with a relatively small dataset makes the model built is considered to have good capabilities. The ability is also confirmed by the high ROC-AUC value with a value of 0.965
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.

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