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Dwiza Riana
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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
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Search results for , issue "Volume 3 No. 4, December 2025" : 5 Documents clear
Implementation of Image Processing Techniques for Viral Pneumonia Diagnosis Using Chest X-Ray Images Wardani, Yulita Ayu
Journal Medical Informatics Technology Volume 3 No. 4, December 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i4.52

Abstract

The COVID-19 pandemic has increased the need for rapid and accurate diagnostic methods for viral pneumonia diseases. This study employs an experimental approach to analyze the application of image processing techniques for viral pneumonia diagnosis using chest X-ray images. The dataset consists of 100 chest X-ray images obtained from a public Kaggle repository, including normal and viral pneumonia cases. The proposed methodology involves several main stages. Image preprocessing is performed through image enlargement using bilinear interpolation and noise reduction using a median filter to improve image quality. Morphological operations, including erosion and dilation, are applied to enhance lung structures and clarify anatomical contours. Image enhancement is conducted using histogram equalization to improve contrast between healthy and infected regions. Finally, convolution-based edge detection using the Sobel operator is applied to highlight structural boundaries relevant to diagnostic interpretation. This processing framework aims to enhance image clarity and feature visibility, thereby supporting more efficient and consistent analysis of chest X-ray images for viral pneumonia diagnosis.
Identification of Rotten Carrots Using Image Processing with Edge Detection and Convolution Techniques Syofian, Muhammad
Journal Medical Informatics Technology Volume 3 No. 4, December 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i4.53

Abstract

Carrot is one of the agricultural commodities with high nutritional value and a significant market demand. However, its quality can deteriorate due to various factors, one of which is rotting. Early detection of rotting carrots is crucial to prevent economic losses and maintain product quality. The main problem in identifying rotten carrots lies in the need for high precision and the time-consuming nature of manual methods. To address this issue, this research develops an automated method for detecting rotten carrots using image processing techniques. In this study, edge detection and convolution techniques are employed as the primary approaches in image analysis. Edge detection is used to recognize contours and boundaries in carrot images, while convolution techniques are applied to identify patterns of damage and texture differences between rotten and healthy carrots. The research findings indicate that this method is capable of detecting rotten carrots with high accuracy, making it reliable as a tool for sorting and quality assurance in carrot processing.
The Correlation Between Social Support And Self-Esteem Among Final-Year Students At Bhakti Husada University Indonesia Pranatha, Aria; Apriyanty, Vevi
Journal Medical Informatics Technology Volume 3 No. 4, December 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i4.109

Abstract

The World Health Organization (WHO) reports that low self-esteem is a prevalent mental health issue worldwide, affecting an estimated 121 million people, including 5.8% of men and 9.5% of women, with many cases occurring among individuals in their productive years—university students included. The objective of this study was to determine the relationship between social support and self-esteem among final-year nursing students at Bhakti Husada University Indonesia. This study employed a quantitative approach using a correlational descriptive method and a cross-sectional design. The population consisted of 178 final-year nursing students working on their theses in 2022, from which a sample of 113 was selected through proportionate stratified random sampling. The research instruments included a social support questionnaire and a self-esteem questionnaire, and the data were analyzed using the Spearman’s Rank correlation test. The bivariate analysis yielded a p-value of 0.000 and a correlation coefficient (rho) of 0.547, indicating a significant relationship between social support and self-esteem among final-year students. The findings of this study provide practical implications, particularly for students, educators, and academic institutions, by emphasizing the importance of strengthening social support systems to enhance students’ self-esteem, improve coping abilities, and support their academic completion. It is hoped that students will be able to build greater self-confidence, become more open, and develop supportive relationships with family, friends, and academic advisors while facing the challenges of completing their theses.
Comparison of Naive Bayes and Decision Tree Methods in Breast Cancer Classification Sulistyowati, Daning Nur; Hadianti, Sri; Mayangky, Nissa Almira
Journal Medical Informatics Technology Volume 3 No. 4, December 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i4.112

Abstract

The early diagnosis of breast cancer is a critical factor in improving recovery rates and reducing cancer-related mortality. This study aims to compare the performance of two widely used machine learning algorithms in medical data classification Naive Bayes and Decision Tree in detecting breast cancer using the Breast Cancer Wisconsin (Diagnostic) dataset. The dataset consists of 569 samples with 30 numerical features and one target label. The methodology includes data preprocessing, model training, and performance evaluation using six metrics: accuracy, precision, recall, F1-score, AUC, and MCC. Naive Bayes achieved higher performance, with 96.5% accuracy, 97.6% precision, 93.0% recall, 95.2% F1-score, 0.997 AUC, and 0.925 MCC, compared to Decision Tree with 93.9% accuracy, 90.9% precision, 93.0% recall, 92.0% F1-score, 0.936 AUC, and 0.87 MCC. Confusion matrix and ROC curve analyses support these results, particularly in minimizing classification errors. While Decision Tree offers better interpretability, Naive Bayes may be more suitable for early breast cancer detection under similar dataset conditions. Future studies could explore ensemble approaches to combine the strengths of both methods.
Enhancing the Quality of Digital Panoramic Radiographs with Median Filtering and Histogram Equalization Techniques Yusuf, Moh.; Novianty, Shella Indri; Maharani, Bella
Journal Medical Informatics Technology Volume 3 No. 4, December 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i4.114

Abstract

Digital panoramic radiographs often suffer from noise and reduced contrast, which can compromise diagnostic accuracy and treatment planning. Conventional enhancement methods rely on default Carestream software with manual adjustments, which may be inconsistent and time-consuming. This study aims to improve the quality of digital panoramic radiographs by applying Median Filtering (MF) to reduce noise and Histogram Equalization (HE) to enhance contrast using MATLAB R2022a. An analytical experimental design was conducted on 155 digital panoramic radiograph images, sampled from a total population of 254 images collected between July 2021 and July 2022 at the Dental Radiology Installation, Islamic Dental and Oral Education Hospital of Sultan Agung Semarang (RSIGMP-SA), using the Slovin formula. Radiograph files in DICOM format were converted to JPEG for analysis. Image quality was evaluated using Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), and statistical significance was analyzed with the paired T-test in SPSS. The results indicated normal distribution for all parameters (p>0.05) and significant improvements in SNR and CNR after enhancement (T=-14.426 for SNR, T=-41.673 for CNR, p<0.05). These findings demonstrate that MF effectively enhances image sharpness, while HE improves contrast, resulting in clearer and more diagnostically reliable panoramic radiographs. The proposed approach provides a standardized and reproducible method for image quality improvement and offers potential support for clinical decision-making.

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