cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Purwono
Contact Email
purwono@ptti.web.id
Phone
+6282113940427
Journal Mail Official
jahir@ptti.web.id
Editorial Address
Jl. Empu Sedah No. 12, Pringwulung, Condongcatur, Kec. Depok, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
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 6 Documents
 1 
Search results for , issue "Vol. 2 No. 3 (2024)" : 6 Documents clear
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
A Bibliometric Analysis of Knowledge Distillation in Medical Image Segmentation Muntiari, Novita Ranti; Rania Majdoubi; Rajiansyah
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.v2i3.297

Abstract

This study conducts a bibliometric analysis and systematic review to examine research trends in the application of knowledge distillation for medical image segmentation. A total of 806 studies from 343 distinct sources, published between 2019 and 2023, were analyzed using Publish or Perish and VOSviewer, with data retrieved from Scopus and Google Scholar. The findings indicate a rising trend in publications indexed in Scopus, whereas a decline was observed in Google Scholar. Citation analysis revealed that the United States and China emerged as the leading contributors in terms of both publication volume and citation impact. Previous research predominantly focused on optimizing knowledge distillation techniques and their implementation in resource-constrained devices. Keyword analysis demonstrated that medical image segmentation appeared most frequently with 144 occurrences, followed by medical imaging with 110 occurrences. This study highlights emerging research opportunities, particularly in leveraging knowledge distillation for U-Net architectures with large-scale datasets and integrating transformer models to enhance medical image segmentation performance
Virus Host Prediction with Metagenomic Features using Support Vector Machine Algorithm and Grid Search Cross Validation Optimization Purwono, Purwono; Annastasya Nabila Elsa Wulandari; Novieta Hardeani Sari
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.v2i3.298

Abstract

Viruses and bacteria continue to evolve alongside humans. Viruses are spreading too fast and causing a huge loss of life in the world. Viruses play an important role as dangerous pathogens that continue to spread various infectious diseases. Metegenomics is the application of large sequencing technology to genetic material obtained directly from one or more environmental samples, resulting in at least 50Mb random samples and multiple long sequences. It is important to identify the origin of the virus to prevent the spread of outbreaks. Understanding the biology of these viruses and how they affect their ecosystems depends on knowing which host they infect. We can use metagenomic features derived from the viral genome to determine the type of virus host. The activity of predicting virus hosts has traditionally taken a lot of time and effort in the process. Technology can be one of the solutions that can be used to predict virus host types. One of the technologies that can be used is machine learning. We chose one of the machine learning algorithms, SVM, to predict viral hosts with metagenomics features, namely genome size, GC% and number of CDS from viral genomes derived from 7326 viral genomes. The SVM model was further optimised with GS and K-CV methods. This optimisation resulted in an increase in the accuracy value of the model when predicting virus hosts from 80% to 84%.
Potential Use of U-Net and Fuzzy Logic in Diabetic Foot Ulcer Segmentation: A Comprehensive Review Rachman Hidayat; Annastasya Nabila Elsa Wulandari; Purwono, Purwono; Khoirun Nisa
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.v2i3.299

Abstract

Diabetic foot ulcer (DFU) image segmentation is still an interesting concern of researchers. Various new deep learning-based methods have been proposed to handle this image segmentation problem. Some research problems that are still faced by many researchers are dataset problems that are considered limited and need further clinical trials. The challenges of data problems include heterogeneity and image quality variations in the shape of skin lesions and subjectivity when annotating. The evaluation results from previous studies also show a considerable difference where there are still low accuracy results, but also too high accuracy is still found so that it is considered to have the potential for overfitting. As a result of the review of various related studies, there is an interesting potential of applying fuzzy logic to the U-Net architecture. This architecture has become very popular because it is widely used in medical image segmentation. The application of fuzzy logic can be applied to the U-Net architecture such as encoder, decoder, skip connection to adjust various U-Net parameters.
Classification of Skin Disease Images Using K-Nearest Neighbour (KNN) Ari Peryanto; Susanto, Dwi; Jihad, Bagus Hayatul
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.v2i3.300

Abstract

The skin is the outermost part of the human body that is often exposed to the environment, so it is easy to experience disease disorders. Some of the skin diseases that are often contracted in humans are ulcers, herpes, and warts. Untreated skin diseases will be very annoying because of the sensation of itching so it can cause irritation and inflammation. The ability to classify skin diseases using technology is one solution. This study uses the K-Nearest Neighbour (KNN) method to detect images of skin diseases. KNN is one of the machine learning methods with a calculation method based on the proximity of k. KNN was chosen because it is fast and has high-accuracy results. The results of the research that has been carried out have obtained results of accuracy of 63%, precision of 63%, recall of 63%, and F1 Score of 63%. From the results of the study, it can be concluded that disease detection using KNN has been successfully applied and can be used in classification.

Page 1 of 1 | Total Record : 6

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 3 No. 1 (2025) Vol. 2 No. 3 (2024) Vol. 2 No. 2 (2024) Vol. 2 No. 1 (2024) Vol. 1 No. 3 (2023) Vol. 1 No. 2 (2023) Vol. 1 No. 1 (2023)