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 1 No. 4, December 2023" : 5 Documents clear
Low Tuberculosis Screening among Household Family Members of Tuberculosis Patients in Banyuarang and Sidowarek Farid, Muhammad Rifqo Hafidzudin; Rananda, Arif; Aflahudin, Muhammad Ahda Naufal; Musalim, Dian Anggraini Permatasari; Hariftyani, Arisvia Sukma; Hanani, Nadya Kelfinta; Rofiq, Rodia Amanata; Aulia, Shazia Hafazhah; Sidqoh, Aida Badi’atus; Hewiz, Alya Shafira
Journal Medical Informatics Technology Volume 1 No. 4, December 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i4.19

Abstract

Early tuberculosis detection is vital, necessitating widespread screening. The WHO's End Tuberculosis strategy aims to combat this epidemic. Active screening is critical for identifying asymptomatic individuals at risk. Data from Pulorejo Primary Health Center, Jombang, indicates a low 10% coverage of suspected cases in 2021, particularly among household contacts, resulting in continued transmission, late detection, post-treatment symptoms, and even death. Therefore, this study was conducted to determine the number of screening participation of households of tuberculosis patients in Banyuarang and Sidowarek Village. This research is a descriptive observational. The data collected was primary data from questionnaires. The study population consisted of households of tuberculosis patients in the Banyuarang and Sidowarek Villages, Jombang Regency. Data collected from 12 respondents showed the prevalent characteristics among the 12 respondents were predominantly female, adult age, high school education, working, limited knowledge about tuberculosis, and easy access to healthcare services. Among the 12 respondents in Banyuarang and Sidowarek, 9 respondents had never been screened, while 3 respondents had undergone screening. The primary reasons for respondents not undergoing screening were lack of awareness regarding the necessity of screening and busy schedules.
Relevance of e-Health Needs and Usage in Indonesia Chairul, Yasrizal; Aziz, Faruq; Hadianti, Sri
Journal Medical Informatics Technology Volume 1 No. 4, December 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i4.20

Abstract

The eHealth application can be used for healthcare, supervision, literature, education, and research. It is a cost-efficient and secure application based on information and communication technology for the health and medical fields. The use of Information and Communication Technology (ICT) as an infrastructure or medium that connects hospitals and health centers using the eHealth electronic health application is the key problem facing the implementation of eHealth on a worldwide scale. eHealth is an ICT-based application for the healthcare industry and one of the Action Plans of the World Summit on the Information Society (WSIS) Geneva 2003. The goal of using the eHealth app is to increase patient access, medical process efficiency, effectiveness, and process quality. This covers the administration of medical services provided by hospitals, clinics, health centers, medical professionals (including therapists and doctors), laboratories, pharmacies, and insurance
Hepatitis Prediction Using K-NN, Naive Bayes, Support Vector Machine, Multilayer Perceptron and Random Forest, Gradient Boosting, K-Means Dwi Saputra, Heru; Efendi, Ade Irfan Efendi; Rudini, Edwin; Riana, Dwiza; Hewiz, Alya Shafira
Journal Medical Informatics Technology Volume 1 No. 4, December 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i4.21

Abstract

Hepatitis is a serious disease that causes death throughout the world. It is responsible for inflammation in the human liver. If we manage to detect this life-threatening disease early, we can save many lives from it. In this research paper, we predict hepatitis disease using data mining techniques. We have attempted to propose a feasible approach to improve the performance of our prediction models in our research. We address the problem of missing values in the dataset by replacing them with the mean value. Nine algorithms were applied to the hepatitis disease dataset to calculate prediction accuracy. We measure accuracy, precision, recall, ROC and best score, and we compare them with random search hyperparameter tuning. It is hoped that by using them we will find the optimal combination of hyperparameters to improve the performance of machine learning models which helps us compare the performance of classification models.
Tubercolusis Segmentation Based on X-ray Images Priyono, Eko; Fatah, Teddy Al; Ma’mun, Sukrul; Aziz, Faruq
Journal Medical Informatics Technology Volume 1 No. 4, December 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i4.22

Abstract

Tuberculosis or TB is an infectious disease caused by the bacteria Mycobacterium tubercolusis. This disease usually attacks the lungs, but can also affect other organs such as the kidneys, bones and brain. TB is highly contagious, and can spread through the air when someone who is infected coughs or sneezes. Risk factors that can increase a person's chances of developing TB include a weak immune system, such as people with AIDS, diabetes, or people taking immunosuppressant drugs. And people who live or work in environments with high rates of TB transmission are also at risk of infection. Symptoms of TB are usually a cough that lasts more than three weeks, unexplained weight loss, fever, night sweats and persistent fatigue. In more severe cases, TB can cause coughing up blood, chest pain and difficulty breathing. One of the examination tools that can be used to detect TB disease is x-rays. Which produces X-Rays to help and confirm the diagnosis of TB disease, to see the chest part of the body which is used as medical record documentation. In X-ray photos, random dark and light spots of noise are often found which are caused by several factors. Based on the facts above, image segmentation is an important task for doctors in diagnosing disease. Automatic detection or segmentation of lung images from chest x-ray images is the initial stage of the diagnosis process. This research aims to implement a segmentation method to determine edge detection in clearer images using several segmentation methods, namely the Canny Edge Detection method, Sobel reading chest x-ray results for tuberculosis. And canny edge detection with segmented RGB image (otsu's thresholding) produces the highest value, namely 230,466.0 pixels and a lesion volume of 14,818.625 mm3.
Cervical Cancer Papsmear Classification through Meta-Learning Technique using Convolution Neural Networks. Mahendra, M; Jumadi, J; Riana, Dwiza
Journal Medical Informatics Technology Volume 1 No. 4, December 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i4.23

Abstract

This study uses convolutional neural networks (CNNs) and meta-learning techniques to create an accurate and efficient model for classifying the risk factors of cervical cancer. The dataset includes four types of cervical lesions, and the main objective is to categorize these lesions as either benign or malignant. This classification is essential for early and succesfull treatment of cervical cancer. The challenge arises from the complexity and variations in the images, resulting in the inability of conventional machine learning and deep learning approaches to provide correct classifications. Meta ensemble learning approaches are employed to improve the model's classification accuracy. The dataset of cervical cancer risk factors is preprocessed before being used to train and evaluate numerous CNNs utilizing pre-trained models and various architectures. Subsequently, a meta-learning is employed to optimize the learning process, and used to aggregate the outputs of the multiple CNNs. Moreover, the assessment findings show the model achieves high accuracy and effectiveness. Finally, the suggested model's accuracy score will be contrasted against the current cutting-edge methods used by other existing systems.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2023 2023


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