cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Dwiza Riana
Contact Email
dwizariana22@gmail.com
Phone
+6281771998
Journal Mail Official
jmedinftech@gmail.com
Editorial Address
Jl. Raya Jatiwaringin No.2, Jakarta-13620, Indonesia
Location
Kota padang,
Sumatera barat
INDONESIA
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. 3, September 2025" : 5 Documents clear
Measuring User Acceptance Of ALODOKTER Application With Technology Acceptance Model To Enhance Health Service Quality Malau, Fransiscus Rolanda; Sakti, Ichtiar Akbar
Journal Medical Informatics Technology Volume 3 No. 3, September 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i3.47

Abstract

ALODOKTER is one quickly evolving application in the healthcare services sector. The purpose of this application is to help medical professionals carry out their jobs more effectively by giving the community rapid and easy access to healthcare services. This study aims to measure user acceptance of the ALODOKTER application using the Technology Acceptance Model (TAM) approach to improve the use and quality of health services. A survey method with a quantitative approach was employed to analyze perceived ease of use (PEU), perceived usefulness (PU), attitude towards use (ATU), behavioral intention to use (BIU), and actual use (AU) of the application. The study involved 41 respondents from various demographic backgrounds. Results show significant relationships between user perception variables, attitudes, and actual use. Correlation analysis revealed strong relationships between PEU, PU, and ATU, with a very strong correlation between ATU and BIU. Linear regression analysis indicated that BIU was the strongest predictor of actual use of the app (β = 1.066, p < 0.01), followed by PU (β = 0.628, p < 0.01). The regression model explained 38.7% of the variance in actual use. Cronbach's Alpha coefficients for all scales exceeded 0.9, indicating high reliability of the instruments used. This research suggests that ALODOKTER developers should focus on enhancing the perceived usefulness and ease of use of the application to increase acceptance and use. The study's limitations include a small sample size and reliance on self-reporting, suggesting the need for further research with larger samples and more diverse methods.
Optimization of Melanoma Skin Cancer Detection through Data Magnification, Filter Preprocessing, Image Enhancement, and Convolutional Techniques Fatimah Asmita Rani
Journal Medical Informatics Technology Volume 3 No. 3, September 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i3.48

Abstract

Melanoma skin cancer is one of the most aggressive forms of cancer, requiring early detection to improve patient outcomes. This study evaluates three image processing methods—Laplacian, Box Blur, and Edge Detection—used in melanoma detection, analyzing their performance using Mean Squared Error (MSE) and Structural Similarity Index (SSIM) metrics. Among these, Box Blur demonstrated the best overall performance with the lowest average MSE (104.16), indicating minimal distortion in the processed images. Additionally, it achieved the highest SSIM score (0.851), suggesting that it best preserved the structural integrity of the images, making it the most effective in maintaining both quality and important diagnostic details. In contrast, Edge Detection produced the highest MSE (108.02) and a negative SSIM score (-0.016), significantly distorting image structure and making it less suitable for melanoma detection. Laplacian, while moderate in performance, did not outperform Box Blur, with an MSE of 106.99 and an SSIM of 0.175. These results highlight Box Blur as the most reliable technique for melanoma image analysis, ensuring both clarity and structural preservation. By effectively enhancing diagnostic features and reducing errors, Box Blur offers a valuable tool for clinicians aiming to improve diagnostic accuracy in melanoma detection.
Optimisation of Image Morphology Operations with Enhancement and Convolution in Tomato Leaf Disease Symptom Recognition Indra, Muhamad
Journal Medical Informatics Technology Volume 3 No. 3, September 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i3.50

Abstract

Tomato (Solanum lycopersicum) is an important horticultural crop that is highly susceptible to various leaf diseases such as leaf spot, bacterial wilt, and fruit rot, which significantly reduce yield and quality. This study applies digital image processing techniques including pre-processing, morphology, enhancement, and convolution to improve the recognition of disease symptoms on tomato leaves. Pre-processing using grayscale conversion and median blur effectively reduces noise and sharpens essential details, while morphological operations (erosion and dilation) highlight structural features of infected areas. Enhancement techniques increase image contrast, making the distinction between healthy and diseased tissue more visible. Convolution methods with kernels such as Sobel and Gabor further emphasize edges and texture patterns of leaf lesions. Experimental results show that these methods improve pixel intensity distribution and enhance the visibility of disease symptoms, thereby increasing diagnostic accuracy. The integration of these techniques demonstrates the potential for early detection and classification of tomato leaf diseases, enabling more effective disease management and prevention of crop losses.
Optimizing Image Quality for Dog Skin Disease Diagnosis: Bacterial, Fungal, and Hypersensitivity Cases with MATLAB Puspitaningtyas, Mery Oktaviyanti; Na`am, Jufriadif
Journal Medical Informatics Technology Volume 3 No. 3, September 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i3.54

Abstract

Skin diseases in dogs, such as hypersensitive dermatitis, fungal infections, and bacterial dermatoses, present diverse clinical signs that complicate diagnosis in veterinary practice. This study employs MATLAB as an image-processing tool to enhance diagnostic accuracy through a structured pipeline. A dataset of 500 canine skin images obtained from Kaggle was processed using enlargement, histogram equalization, Gaussian filtering, and Sobel convolution. These methods improved image quality by enhancing contrast, reducing noise, and clarifying lesion boundaries. The experimental results demonstrate that the processed images allow veterinarians to more easily detect key diagnostic features, including changes in lesion texture, color, and shape. Enhanced visual clarity supports faster identification of disease patterns and reduces diagnostic ambiguity in clinical settings. This study highlights the potential of MATLAB-based image processing as an effective decision-support tool for veterinary dermatology, enabling quicker and more reliable treatment planning. Future work may integrate deep learning classification to further automate disease recognition.
Identification of Cell Images in Pap Smear Using GLCM and Classification Methods in Machine Learning Agustino, Rano; Fauziah, Prima Nanda
Journal Medical Informatics Technology Volume 3 No. 3, September 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i3.85

Abstract

Early detection of cervical cancer is critical for improving patient outcomes, and accurate classification of Pap smear images supports clinical decision-making. This study aimed to improve cervical cancer diagnosis by classifying Pap smear images using texture features. A dataset of 250 images across five classes underwent preprocessing including grayscale conversion and noise removal. Texture features such as contrast, dissimilarity, homogeneity, energy, correlation, and Angular Second Moment (ASM) were extracted using the Gray-Level Co-occurrence Matrix (GLCM). These features were then used to train and evaluate machine learning algorithms: Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), and Neural Networks (NN). The Decision Tree model achieved the highest accuracy of 95%, outperforming Neural Networks which reached 74%. Ensemble methods like RF and GB showed robust performance across classes. These results demonstrate the effectiveness of GLCM-based feature extraction combined with Decision Tree classification for accurate and reliable Pap smear image analysis. This approach offers valuable insights for enhancing clinical decision support in cervical cancer diagnosis.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Volume 3 No. 4, December 2025 Volume 3 No. 3, September 2025 Volume 3 No. 2, June 2025 Volume 3 No. 1, March 2025 Volume 2 No. 4, December 2024 Volume 2 No. 3, September 2024 Volume 2 No. 2, June 2024 Volume 2 No. 1, March 2024 Volume 1 No. 4, December 2023 Volume 1 No. 3, September 2023 Volume 1 No. 2, June 2023 Volume 1 No. 1, March 2023 More Issue