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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 65 Documents
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Enhancing Thorax Images Using Fuzzy Logic Based Techniques Marfiana, Duwi Lufita
Journal Medical Informatics Technology Volume 4 No. 1, March 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i1.46

Abstract

Enhancing the quality of thoracic X-ray images is crucial for accurate medical diagnosis; however, conventional enhancement methods often struggle to reduce noise while preserving important edge structures and anatomical details. This study proposes a hybrid image enhancement framework that integrates median neighborhood filtering, convolution processing, fuzzy logic-based edge detection, and morphological operations to improve image clarity and structural definition. The proposed pipeline begins with median neighborhood filtering to reduce noise while preserving essential image structures. The filtered image is then processed using convolution to enhance feature representation and prepare the data for edge detection. Subsequently, fuzzy logic-based edge detection is applied to handle intensity variations and uncertainty, enabling adaptive detection of faint and overlapping edges. Finally, morphological operations are used to refine edge continuity and remove small artifacts, resulting in clearer anatomical boundaries. Experimental results demonstrate that the proposed method effectively reduces noise while maintaining structural integrity, as indicated by stable pixel value transformations after filtering and improved edge clarity in visual comparisons. The method shows better performance in preserving continuous edge structures and detecting subtle thoracic features compared to conventional approaches. In conclusion, the integration of median filtering, convolution processing, fuzzy logic-based edge detection, and morphological refinement provides an effective framework for enhancing thoracic medical images and supports more reliable interpretation in medical imaging applications.
Early Diabetes Detection Using Machine Learning Models: A Case Study from Indonesian Clinical Data Chairul, Yasrizal; Muhammad Haris
Journal Medical Informatics Technology Volume 4 No. 1, March 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i1.80

Abstract

Diabetes is a major health problem that can significantly reduce life expectancy and increase the risk of serious complications such as kidney failure, stroke, and cardiovascular disease. Therefore, early detection is essential to prevent the progression of the disease. This study proposes a machine learning-based approach for early diabetes detection using a private dataset obtained from RSUP Persahabatan General Hospital in Jakarta, Indonesia. The dataset consists of 501 patient records with clinical and laboratory features extracted from the hospital’s electronic medical record system. Several machine learning algorithms were implemented and compared, including Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, Naïve Bayes, Extreme Gradient Boosting, Ensemble methods, and Artificial Neural Networks. Feature selection was performed using ANOVA, and hyperparameter optimization was applied using GridSearchCV to improve model performance. The experimental results show that the Artificial Neural Network model achieved the best performance with an accuracy of 0.86 (86%). Statistical analysis using logistic regression identified systolic blood pressure, diastolic blood pressure, age, HDL cholesterol, and leukocyte levels as the most significant risk factors associated with diabetes. These findings demonstrate the potential of machine learning techniques to support early diabetes detection using clinical data from Indonesian healthcare settings.
Evaluation of Pap Smear Nucleus Cell Image Segmentation: The Impact of Enhancement Processes on Segmentation Result Nainggolan, Esron; Merlina, Nita; Setiadi, Farisya
Journal Medical Informatics Technology Volume 4 No. 1, March 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i1.93

Abstract

Accurate nucleus segmentation is vital for automated cervical cancer diagnosis, yet it remains challenging due to overlapping cells and uneven lighting. This study evaluates Polynomial Contrast Enhancement (PCE) using a second-degree polynomial function to improve segmentation on 100 images from the RepomedUNM dataset. The pipeline integrates grayscale conversion, Gaussian blur, and PCE prior to Canny edge detection. Results demonstrate near-perfect Precision (0.9999–1.0000) across all categories (Normal, H-SIL, L-SIL, and Koilocyt), effectively eliminating false positives. However, Recall and Accuracy remained low (max 0.0634 in H-SIL), a technical consequence of Canny’s limitation in capturing thin boundaries versus solid nuclear areas. The study’s novelty lies in the application of second-degree PCE to stabilize intensity variations across multiple diagnostic categories. While PCE ensures exceptional localization precision, future systems should integrate deep learning to enhance recall in complex overlapping structures.
Validation of the pSUAPP Questionnaire and User Experience Evaluation of the Satu Sehat Health Application in Indonesia Eriyandi, Vina; Tanebeth, Riki Daniel; Riana, Dwiza; Hadianti, Sri
Journal Medical Informatics Technology Volume 4 No. 1, March 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i1.110

Abstract

The increasing use of digital health applications in Indonesia requires valid and reliable instruments to evaluate usability and user experience. This study aims to adapt and validate the pSUAPP questionnaire in the Indonesian context and to assess the usability of the Satu Sehat application. A cross-sectional validation study was conducted from May to June 2025 involving 102 active users of the Satu Sehat application, with 90 respondents included in the final psychometric analysis. The adapted pSUAPP questionnaire consists of 27 items covering four domains: first contact, registration, features, and overall user experience. Reliability and validity were assessed using Cronbach’s alpha, correlation analysis with SUS, and exploratory factor analysis (EFA). The results showed that the mean pSUAPP score was 67.76 (SD = 18.39), indicating moderate usability. The registration domain obtained the highest score (mean = 87.50; SD = 12.50), while the feature (mean = 70.36) and experience (mean = 69.62) domains showed relatively lower scores. The questionnaire demonstrated high internal consistency, with strong correlations across domains and with SUS. EFA identified four factors explaining 76.7% of the total variance. No significant differences were observed across sociodemographic characteristics. In conclusion, the Indonesian version of the pSUAPP questionnaire is a valid and reliable instrument for evaluating digital health applications. While the Satu Sehat application performs well in registration, improvements are needed in monitoring features and user experience to support long-term engagement.
Differences in Contrast Quality of Digital Panoramic Radiographs Before and After Contrast Stretching Yusuf, Moh; Kartika S, Rina; Putri, Dinanti Irwina
Journal Medical Informatics Technology Volume 4 No. 1, March 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i1.115

Abstract

Digital panoramic radiography is widely used for diagnosis and treatment planning because it provides comprehensive information on dental and maxillofacial anatomical structures in digital form that can be directly visualized on a computer screen. However, the quality of panoramic radiographs may decrease due to noise, inadequate density, and low contrast, which can affect diagnostic interpretation. The contrast stretching method can be applied to address this problem by increasing image contrast and reducing noise, thereby improving the visibility of objects and anatomical boundaries in radiographic images. This study aimed to determine the effect of the contrast stretching method on the quality of digital panoramic radiographs. A quantitative experimental analysis was conducted using retrospective digital panoramic radiograph data from patients at RSIGMP UNISSULA. A total of 155 digital panoramic radiograph images from July 2021 to July 2022 were selected using the Slovin method. Image quality enhancement was quantitatively evaluated using the Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR) parameters. The obtained data were analyzed using a paired t-test after fulfilling the normality assumption. The results showed significant differences in both SNR and CNR values before and after processing, with a significance value of 0.000 (p < 0.05). Both parameters increased after contrast stretching, indicating improved image contrast and reduced noise in digital panoramic radiographs. These findings demonstrate that contrast stretching is an effective and practical method for improving radiographic quality, which may support radiographers in achieving clearer diagnostic images and provide useful insight for medical imaging system developers in designing image enhancement modules.

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