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Journal of Advanced Health Informatics Research
Published by Peneliti Teknologi Teknik Indonesia
ISSN : -     EISSN : 29856124     DOI : https://doi.org/10.59247/jahir.v1i1
Core Subject : Health, Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering Medicine & Pharmacology Public Health
Journal of Advanced Health Informatics Research (JAHIR) is a scientific journal that focuses on the application of computer science to the health field. JAHIR is a peer-reviewed open-access journal that is published three times a year (April, August and December). The scientific journal is published by Peneliti Teknologi Teknik Indonesia (PTTI). The JAHIR aims to provide a national and international forum for academics, researchers, and professionals to share their ideas on all topics related to Informatics in Healthcare Research
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 36 Documents
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Effectiveness of Cream Preparations Combination of Bay Leaf Extract (Syzygium polyanthum Wight) and Papaya Leaf (Carica Papaya L) as Anti-Inflammation Samodra, Galih; Kusuma, Ikhwan Yudha; Melani, Reina
Journal of Advanced Health Informatics Research Vol. 1 No. 2 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i2.37

Abstract

Bay leaves (Sygyzium polyanthum Wight) and papaya leaves (Carica papaya L) are plants that have anti-inflammatory activity due to their flavonoid compounds. The purpose of this study was to determine the formulation of bay leaf and papaya leaf extracts into cream preparations. The treatment group consisted of 4 groups. The first group is the positive control (voltarene), the second group is the negative control (1% carrageenan). The third group was the group that was given the combination cream treatment of bay leaf and papaya leaf extract formula 2 and the fourth group was the group that was given the cream treatment combination of bay leaf and papaya leaf extract formula 3. The anti-inflammatory test was carried out based on observations for six hours by looking at edema volume and edema percentage. LSD test results showed that the cream formula 2 treatment (anionic) and the cream formula 3 treatment (nonionic) showed a significant difference with the negative control with a significance value of 0.000 (p <0.05). This shows that formula 2 cream treatment (anionic) and formula 3 cream treatment (nonionic) can potentially reduce edema volume and can inhibit edema on the soles of rats induced with carrageenin
Understanding User Sentiment: Analysis of SATUSEHAT Application Reviews on Google Play Store Ardianto, Rian; Marhoon , Hamzah M.
Journal of Advanced Health Informatics Research Vol. 1 No. 2 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i2.44

Abstract

The Covid-19 epidemic has caused substantial changes in Indonesia, causing the government to create SATUSEHAT Mobile, formerly known as PEDULILINDUNGI, which will be formally renamed on March 1, 2023. This software tracks the spread of Covid-19, offers vaccination information, and distinguishes distinct zones. Users can share their location data for travel purposes, allowing Covid-19 patients to be contacted. Sentiment analysis is used to assess SATUSEHAT users' perspectives based on Play Store reviews, with an emphasis on positive and negative comments. Textual data connected to certain items or entities is analyzed and modified using Data Mining techniques. The SATUSEHAT application was tested in this study. The program classified 43 positive and 1662 negative comments correctly, yielding 1705 successful classifications out of 1861 comments. The data from the confusion matrix allowed for the calculation of accuracy, precision, and recall, achieving 92% accuracy, 93% average precision, and 98% average recall. According to the research findings, the Naive Bayes algorithm with TF-IDF Vectorizer is the best at producing positive and negative labels with 92% accuracy, even for unbalanced data. In comparison to other algorithms, Naive Bayes with TF-IDF Vectorizer exhibited good accuracy, indicating a promising topic for further study.
Comparison of Classification and Regression Model Approaches on the Main Causes of Stroke with Symbolic Regression Feyn Qlattice Purwono, Purwono; Agung Budi Prasetio; Burhanuddin bin Mohd Aboobaider
Journal of Advanced Health Informatics Research Vol. 1 No. 2 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i2.87

Abstract

Stroke is one of the deadliest diseases in the world, caused by damage to brain tissue resulting from a blockage in the cerebrovascular system. Proper treatment is essential to avoid worsening complications in patients. Several main triggering factors for stroke include hypertension, obesity, smoking habits, lack of physical activity, excessive alcohol consumption, diabetes, and high cholesterol levels. The advancement of information technology allows for early disease prediction through the utilization of AI and Machine Learning technology. The vast amount of data available on medical and health services worldwide can be maximized to identify risk factors for various diseases, including stroke. Machine learning techniques can be employed to predict the causes of stroke. In this study, we were inspired to use the Feyn Qlattice model approach to address stroke. Both classification and regression models were tested in this study. The results indicate that the classification model performs better, achieving an accuracy rate of 0.95. In contrast, the regression model yielded less satisfactory results, with R2, MAE, and RMSE values considered inadequate. This conclusion is supported by the regression plot and residual plot, both of which indicate suboptimal performance. Hence, maximizing the use of the Feyn Qlattice regression model in datasets related to the causes of stroke is recommended
Understanding the Perception of Adolescent Challenges in Body Mass Index Reduction: A Qualitative Study Suwarsi; Joshepine Lorica; Hastuti, Agustina Sri Oktri
Journal of Advanced Health Informatics Research Vol. 1 No. 3 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i3.39

Abstract

A weight loss program for adolescents needs to be done immediately, considering that the number of adolescents who are obese is increasing. This research contributes to identifying the challenges faced by overweight adolescents in weight loss programs. Data was collected by filling out a questionnaire given directly to the participants. The location where the research was conducted at Yogyakarta City, Indonesia. The questionnaire has been tested for validity and reliability with a value of more than 0.85. This study uses a qualitative research design, with a sample of adolescents aged 15-19 years who are obese. The number of participants is 15; according to the inclusion criteria, the data is taken through in-depth interviews. Results: The difficulty of overweight adolescents in losing weight is due to the lack of parental support in serving food and the lack of support from friends in diet programs. Health programs for adolescents, especially weight loss programs, need to involve peers and support from parents
Artificial Intelligence-Based Mobile Health Solutions in the Health 4.0 Era Ariefah Khairina Islahati; Purwono, Purwono; Bala Putra Dewa
Journal of Advanced Health Informatics Research Vol. 1 No. 3 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i3.166

Abstract

In the Health 4.0 era, technological advances continue to bring major changes to the health care industry. Artificial intelligence (AI)-based Mobile Health (mHealth) solutions are an important innovation that will meet modern needs. As time goes by, modern society is increasingly dependent on health technology in everyday life, especially to manage their health conditions. The use of Mobile Health, especially via smartphone devices, has been proven to provide more personalized and affordable health services. Additionally, AI makes diagnosis and health monitoring easier. In this article, the concept of mHealth AI 4.0 is discussed, with particular emphasis on its critical role in providing responsive, proactive, and patient-focused healthcare. In the Health 4.0 era, it is hoped that we can make a positive contribution to improving the quality of health services by using this solution
Challenges and Opportunities: Integration of Data Science in Cancer Research Through A Literature Review Approach Purwono, Purwono; Ariefah Khairina Islahati; Yuslena Sari; Dewi Astria Faroek; Muhammad Baballe Ahmad
Journal of Advanced Health Informatics Research Vol. 1 No. 3 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i3.167

Abstract

Several research articles in this journal relate to various aspects of cancer, such as treatment, patient outcomes, caregiver responsibilities, and the use of AI and liquid biopsy in cancer research. Covers a wide range of topics, including valuable insights into the latest developments in cancer research as well as potential future opportunities and issues. Several articles discuss the impact of non-coding RNA on gastric cancer, machine learning decision support systems for cancer survival factors, economic impact of cancer mortality, nausea in children diagnosed with cancer, protein-RNA variations in cancer clinical analysis, integration and proteomic data analysis in the context of cancer genomics, personalized cancer medicine, mass spectrometry-based clinical proteomics, cancer proteogenomics, subtype-based This journal provides an in-depth overview of various aspects of current cancer research and future research prospects
The Use of Artificial Intelligence in Disease Diagnosis: A Systematic Literature Review Lutviana; Iis Setiawan Mangkunegara
Journal of Advanced Health Informatics Research Vol. 1 No. 3 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i3.172

Abstract

This paper discusses the crucial role of Artificial Intelligence in improving disease diagnosis and the use of medical data in the era of big data. Through the Systematic Literature Review (SLR) approach, we review recent developments in the application of Machine Learning (ML) to disease diagnosis, evaluate the ML techniques applied, and highlight their impact on healthcare. BP-CapsNet uses the Convolutional Capsule Network (CapsNet) to diagnose cancer with advantages in overcoming invariance to image transformation. The Stacking Classifier achieves 92% accuracy in detecting heart defects with the advantage of combining weak learners. CraftNet, a combination of deep learning and handmade features, demonstrates strong recognition capabilities in cardiovascular disease. ML in infectious diseases demonstrates the ability to process big data, focusing on bacterial, viral, and tuberculosis infections. In heart disease diagnosis, ML, especially with CNN and DNN, detects disease at an early stage, despite the challenges of data imbalance. The ensemble algorithm for heart disease prediction demonstrates the superiority of categorical medical features, with SVM and AdaBoost as suitable methods. A new CNN for wide QRS complex tachycardia provides accurate results. In vestibular disease, five ML algorithms provide satisfactory results, with SVM as the best. These findings detail the development of ML in disease diagnosis, highlighting future challenges and opportunities in its use
Exploration of Machine Learning Methods in Medical Disease Prediction: A Systematic Literature Review Ria Suci Nurhalizah; Hadi Jayusman; Purwatiningsih
Journal of Advanced Health Informatics Research Vol. 1 No. 3 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i3.174

Abstract

Exploration of Machine Learning methods in the systematic literature shows successful applications in disease diagnosis, disease prediction, and treatment planning. This literature only includes discussions on Classification methods consisting of Support Vector Machine(SVM), Naïve Bayes, Nearest Neighbors and Neural Network(NN) and Regression consisting of Decision Tree, Linear Regression, Random Forest Ensemble Methods, and Neural Network(NN). Clustering which consists of K-Means Clustering, Artificial Neural Network (ANN), Gaussian Mixture, Neural Network (NN) and Dimensionality reduction which consists of Principal Component Analysis (PCA). In the context of healthcare, the importance of sustainability, ethics, and data security are key factors. This research uses Systematic Literature Review (SLR) to explore Machine Learning methods in the medical context and recommends Support Vector Machine, Random Forest, and Neural Networks as effective methods. By exploring 300 papers and selecting 57 papers for discussion of machine learning methods in medical disease prediction. Method selection should be tailored to the dataset characteristics and disease prediction goals, while prioritizing
Enhancing Nursing Students' Long-term Retention and Engagement in Medical Terminology through Mnemonic-Enhanced Multimedia Mobile Learning Barlian Kristanto; Thanee Glomjai; Diannike Putri
Journal of Advanced Health Informatics Research Vol. 2 No. 1 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i1.178

Abstract

Medical terminology poses a significant learning obstacle for numerous nursing students who rely heavily on textbook definitions. However, these definitions often lack the necessary visual context to facilitate lasting comprehension. In order to enhance literacy skills crucial for safe nursing practice, it is important to explore innovative approaches. This study aimed to evaluate the effectiveness of the Picmonic application, which utilizes audiovisual narratives, in improving retention and engagement compared to traditional studying methods. A parallel group randomized controlled trial compared two learning methods among first-year nursing undergraduates at an urban university. Participants were assigned to either a textbook self-directed learning group (n=62) or an equivalent Picmonic content group (n=60), which used mnemonic visual flashcards and quizzes. Assessments were conducted at 5-, 10-, and 15-week intervals, with additional delayed testing at 1- and 3-months to examine knowledge acquisition and persistence, and to assess the effectiveness of the learning system. Students who used Picmonic had higher average test scores compared to the control group at various measurement points (p < .001). The differences in group means increased over longer intervals, suggesting that the use of multimedia in Picmonic helped with long-term recall. Picmonic users also expressed high satisfaction, voluntarily used the system, and provided positive feedback in focus groups, indicating a preference for the mnemonic-enhanced methodology (p < .001). Multimedia mnemonic educational systems, such as Picmonic, enhance medical terminology retention and engagement of nursing students compared to traditional strategies. This finding has significant implications for instructional design and clinical preparation.
Implementation of Intelligent Pneumonia Detection Model, Using Convolutional Neural Network (CNN) and InceptionV4 Transfer Learning Fine Tuning Anggit Wirasto; Purwono, Purwono; Muhammad Baballe Ahmad
Journal of Advanced Health Informatics Research Vol. 2 No. 1 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i1.180

Abstract

In Pneumonia is a type of contagious lung infection that has caused many human deaths in the form of inflammation of the alveoli. Based on WHO data, pneumonia is a type of acute infection that has caused more than 450 million cases and 4 million deaths each year. Covid-19 is one of the global pandemics that triggered many pneumonia incidents. Chest X-rays (CXR) are an important part of patient care. Radiologists can use CXR features to determine the type of pneumonia and the underlying pathogenesis. Machine learning and deep learning technologies are used to automatically detect various human diseases, thus ensuring smart healthcare. CXR features are more suitable to be analyzed by convolutional neural network (CNN). This algorithm is one of the typical deep learning architectures that has strong characteristics that are widely applied in the healthcare field. This study aims to develop a deep learning-based paradigm to distinguish Covid-19 patients from healthy and normal individuals by analyzing the presence of pneumonia disease symptoms on the CXR. This research provides an approach to the use of InceptionV4 transfer learning type in performing classification on CXR images. There are three main approaches carried out, namely making a standard CNN model, optimizing transfer learning xceptiion and fine tuning. The performance metrics results show a recall value close to 100% with a model accuracy value of 88%. Achieving a high enough recall value with a relatively small dataset makes the model built is considered to have good capabilities. The ability is also confirmed by the high ROC-AUC value with a value of 0.965

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