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Journal Medical Informatics Technology
Published by Asia Pacific Network for Sustainable Agriculture, Food and Energy
ISSN : 29887003     EISSN : 29887003     DOI : https://doi.org/10.37034/medinftech
Core Subject : Health, Science, Engineering,
Computer Science & IT Dentistry Engineering Medicine & Pharmacology Public Health
Journal Medical Informatics Technology publishes papers on innovative applications, development of new technologies and efficient solutions in Health Professions, Medicine, Neuroscience, Nursing, Dentistry, Immunology, Pharmacology, Toxicology, Psychology, Pharmaceutics, Medical Records, Disease Informatics, Medical Imaging and scientific research to improve knowledge and practice in the field of Medical.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 5 Documents
 1 
Search results for , issue "Volume 3 No. 2, June 2025" : 5 Documents clear
Determinants of User Acceptance of the Halodoc Application: An Analysis of User Experience and User Satisfaction Aprianto, Kasiful; Ibrahim, Andi
Journal Medical Informatics Technology Volume 3 No. 2, June 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i2.41

Abstract

Halodoc is one of the leading mobile health (mHealth) applications in Indonesia, offering services such as online doctor consultations, medicine delivery, and health information. This study examines the factors influencing user acceptance of the Halodoc app, focusing on the roles of user experience and satisfaction. The research involved a survey of 81 Halodoc users, followed by validity and reliability testing of the research instruments. Results showed that most items had high validity, with correlation values ranging from 0.775 to 0.851 for user acceptance, and above 0.75 for user experience (except one item). Reliability was also high, with Cronbach’s Alpha values exceeding 0.8 across categories. The highest average score was found in user satisfaction (21.77), indicating consistently high levels of satisfaction. Significant correlations were observed among user acceptance, user experience, service quality, and user satisfaction—most notably between user acceptance and satisfaction (0.8314). Regression analysis identified user experience and satisfaction as significant predictors of user acceptance, accounting for 74.4% of the variance. In contrast, service quality did not show a significant effect. The final regression model after stepwise elimination confirmed the strong influence of user experience (coefficient = 0.3513) and satisfaction (coefficient = 0.4399). These findings highlight the importance of enhancing user experience and satisfaction to increase user acceptance of mHealth applications like Halodoc.
The role of Salmon DNA in Skin Regeneration and Anti-Aging Camilia, Anita; AP, Gadila; Dwiputri, Maliya Finda; Arief, Skolastika Faustina Ivana; Setyadi, Yudha Putra
Journal Medical Informatics Technology Volume 3 No. 2, June 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i2.95

Abstract

Salmon DNA, particularly its polydeoxyribonucleotide (PDRN) content, has attracted attention in dermatology due to its potential in skin regeneration and anti-aging applications. This review aims to summarize various research findings on the effectiveness of PDRN. The study was conducted through a literature review of articles published between 2016 and 2023. Findings indicate that PDRN works by activating adenosine A2a receptors, stimulating angiogenesis, cell proliferation, and reducing inflammation. Its combination with vitamin C and niacinamide enhances antioxidant effects and helps preserve the extracellular matrix. Furthermore, the use of long-chain polynucleotide fillers has been shown to significantly improve pore size, skin texture, wrinkles, and sagging, with minimal side effects. In conclusion, PDRN from salmon DNA is a promising bioactive agent for skin rejuvenation, wound healing, and anti-aging therapy in modern dermatological practice.
Effectiveness of Sardine Consumption in Managing Dysmenorrhea and Anemia in Adolescent Girls Mulyana, Hilman; Daryanti, Eneng
Journal Medical Informatics Technology Volume 3 No. 2, June 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i2.96

Abstract

Dysmenorrhea and anemia are common issues affecting adolescent girls, often requiring effective yet accessible non-pharmacological interventions. This study aimed to evaluate the effectiveness of omega-3 and iron (Fe) content in canned sardines in reducing menstrual pain and improving hemoglobin levels. A quasi-experimental design with a non-randomized pre-test and post-test control group was employed. The participants were 34 adolescent girls from SMK Bhakti Kencana Tasikmalaya experiencing dysmenorrhea, divided equally into intervention and control groups. Pain levels were measured using the Numeric Rating Scale (NRS), and hemoglobin levels were assessed using the Quick Check Set Hemoglobin Testing System. The intervention group consumed canned sardines for three consecutive days prior to the onset of dysmenorrhea, while the control group received conventional health education without any dietary intervention. The results showed no significant changes in pain or hemoglobin levels in the control group, while the intervention group demonstrated significant improvements in both outcomes. These findings suggest that the omega-3 and iron content in canned sardines is effective in reducing dysmenorrhea pain and increasing hemoglobin levels, thereby helping to prevent anemia in adolescent girls.
Image Analysis of Skin Diseases Using DenseNet-121 Architecture Putra, Mahesa; Pioni, Pioni; Rosalina, Alya; Aditya, Diyar; Azhari, Azidan Allen Deva; Hadianti, Sri; Nurfalah, Ridan
Journal Medical Informatics Technology Volume 3 No. 2, June 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i2.99

Abstract

Skin diseases such as dermatitis, psoriasis, and tinea often exhibit similar visual characteristics, which can lead to frequent errors in early diagnosis. Accurate diagnosis is critical, as each disease requires different treatment approaches. This study aims to develop an automated classification model for these three skin diseases using a deep learning approach based on the DenseNet-121 architecture, which consists of 121 layers designed to facilitate efficient feature reuse and gradient flow. The dataset consists of 300 labeled images, evenly distributed among the three disease classes. To enhance model generalization, preprocessing steps were applied, including data normalization and augmentation techniques such as image rotation (±20°), horizontal and vertical flipping, random zooming (range 0.8-1.2×), and brightness adjustment (±20%). The model was trained and validated using a stratified 5-fold cross-validation strategy. Experimental results demonstrated an overall classification accuracy of 94.59%, with high precision and recall scores across all classes. These results indicate the potential of using DenseNet-based deep learning models as decision support tools for early skin disease diagnosis. Further validation with larger datasets and clinical input from dermatologists is recommended to ensure reliability in real-world healthcare settings. Visual comparison through Grad-CAM heatmaps was also conducted to enhance interpretability and validate model focus on relevant skin features.
Evaluating the Role of Data Modalities in Machine Learning Models for Psychiatric Disorder Diagnosis: A Review Çalışkan, Hilal; Ecem Emre, Ilkim
Journal Medical Informatics Technology Volume 3 No. 2, June 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i2.106

Abstract

The increasing prevalence of psychiatric disorders such as depression, bipolar disorder, and post-traumatic stress disorder has drawn attention to the need for more efficient and accurate diagnostic tools. In this context, machine learning offers promising solutions by enabling the analysis of complex and high-dimensional data. This study aims to evaluate the diagnostic performance of ML models applied to various psychiatric disorders by comparing the effectiveness of different data types such as EEG, MRI, video and audio recordings, photographs, survey responses, and clinical data. A total of 44 scientific studies published between 2015 and 2024 were systematically reviewed in accordance with PRISMA 2020 guidelines. The studies included applied ML or deep learning models to adult participants. The results show that the most successful data types varied by disorder. In conclusion, the choice of data type significantly influences the performance of ML models in psychiatric diagnosis. EEG, survey, and clinical data emerged as the most reliable across different conditions, while SVM, Random Forest, and CNN-based models provided the best classification results. These findings offer a valuable reference point for future research and the development of AI-assisted diagnostic tools.

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