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Muhammad Yunus
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International Journal of Healthcare and Information Technology
Published by Politeknik Negeri Jember
ISSN : -     EISSN : 30256933     DOI : 10.25047/ijhitech
Core Subject : Health, Science,
Computer Science & IT Public Health
International Journal of Healthcare and Information Technology (IJHITECH) is published by Politeknik Negeri Jember and managed by Health Information Management, Department of Health. IJHITECH a scientific journal, double blind peer reviewed and open-access journal. IJHITECH is an academic journal organized which focus and scope : medical record, health information management, health information system, health information technology, public health and information technology. IJHITECH provides open access to anyone so that the information and findings in these articles are useful for everyone. This journal article content can be accessed and downloaded for free, free of charge, following the creative commons license used.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 13 Documents
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Search results for , issue "Vol. 3 No. 2 (2026): January" : 13 Documents clear
Understanding the Chinese Medical Students’ AI Acceptance Intention in Healthcare: from a Facilitator and Barrier Perspective Junhao, Fan
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6428

Abstract

Current studies have documented that medical experts’ AI acceptance is influenced by the benefit-and-cost evaluation of its applications in healthcare systems. Guided by the Technology Acceptance Model, the research aim of this study is to explore how positive factors and risk factors might influence Chinese medical students’ AI acceptance intention. This study used a quantitative approach, and the data were collected by a convenience sample from an online survey (N = 419). The statistics software tools, including SPSS 28 and SmartPLS4, were used to perform hypothesis testing. The results revealed that Chinese medical students’ AI acceptance intention was positively related to perceived usefulness and perceived ease of use, but risk factors, which were supposed to inhibit their acceptance intention, showed a positive relationship with AI acceptance intention as well. The perceived usefulness is positively correlated with the risk factors. This means that participants in this study hold a positive attitude towards AI, even though they sensed a slight risk of its application. The theoretical contributions are two-fold. Firstly, this study discussed the impacts of two risk factors and added them to the research model simultaneously. Secondly, this study explored how the perceived usefulness might serve as an antecedent of risk evaluations of AI applications. As for practical implications, this study recommends that in-hand experience with AI practice is crucial for medical students.
Performance Analysis of Naive Bayes Method for Diabetes Diagnosis Amal, Lalu Hadi Ichlasul; Ramlan, Andi Miftahul Jannah; Verdilasari, Devi Kalita; Zari, Inas Fadhilah 'Allam; Mustafa, Muhammad Naufal; Samsir, Mulianingsih; Yunus, Muhammad
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6670

Abstract

Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels, requiring early and accurate detection to prevent long-term complications. Machine learning is increasingly important in data-driven diagnostics, with the Naive Bayes algorithm widely used due to its simplicity, transparency, and efficiency. This study evaluates the classification performance of Naive Bayes for early diabetes screening using a clinical dataset containing incomplete and heterogeneous medical records. The pre-processing involved data cleaning, replacing missing values with the median, labeling patients based on a glyhb threshold ≥6.5%, preventing data leakage, and converting categorical variables into numerical form. Model training was performed with a 70:30 split, and performance was evaluated through accuracy, precision, recall, F1 score, and AUC. The classifier achieved an accuracy of 90.81% and an AUC of 0.919, outperforming standard baseline Naive Bayes implementations which typically report accuracies in the range of 76-78% on similar datasets. Despite this stability, the model showed varying sensitivity in identifying positive diabetes cases, largely due to class imbalance. Therefore, Naive Bayes is considered reliable as a preliminary screening method, but improvements through oversampling or cost-sensitive learning techniques are recommended to enhance recall and ensure more accurate patient identification in future clinical applications.
GeoAI for Precision Public Health in Agrarian Economies: Multi-Disease Risk Profiling in Rice Belt in East Java Budi Fajar Supriyanto; Salihati Hanifa; Nesa Ayu Murthisari Putri; Titin Andriyani Atmojo; Waridad Umais Al Ayyubi
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6646

Abstract

Public health and food security, particularly in the agribusiness sector, are interconnected. As one of the largest rice-producing provinces in Indonesia, East Java faces numerous infectious diseases. To develop a spatial typology of health-agribusiness risks, this study combines epidemiology, agribusiness, and computer science with a Geospatial Artificial Intelligence (GeoAI) approach.The data includes cases of ten infectious diseases (2015–2024), rice harvested area, number of farmers, and district/city population in East Java. Cases were normalized per 100,000 population, and agribusiness indicators were converted to harvested area per farmer ratios. The analysis used internal validation (silhouette score, Davies–Bouldin Index), K-Means clustering, and spatial validation (Moran's I). Results are displayed on OpenStreetMap.Agribusiness can be divided into three main typologies: (1) strong agribusiness with moderate risk; (2) multisector agribusiness with high risk and moderate agribusiness; and (3) moderate agribusiness with a prevalence of lung disease and diarrhea. Moran's I = -0.0263 (p=0.5678), indicating that spatial distribution is not significant. The results suggest that public health does not always correlate with food production intensity. By integrating epidemiology, agribusiness, and GeoAI to support appropriate public health in agricultural areas, this study adds to the international literature.
Design of an Immunization Information System for Monitoring the Achievement of Child Immunization Coverage Prakoso, Bakhtiyar Hadi; Yunus, Muhammad; Suyoso, Gandu Eko Julianto; Vestine, Veronika
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6652

Abstract

Immunization is one of the most effective public health interventions in preventing infectious diseases and reducing mortality and morbidity in the global population, with the potential to prevent 3 to 3.5 million deaths each year from vaccine-preventable diseases. In Low- and Middle-Income Countries (LMICs), including Indonesia, vaccination coverage for children under five years old still shows low numbers.  The low vaccination coverage is partly caused by the use of manual recording systems, which leads to delays in decision-making processes in immunization management. This study seeks to address this issue by developing a UI/UX prototype design for a toddler immunization information system. The proposed UI/UX design is equipped with features to monitor and record immunization activities in a particular region in real time, enabling effective, accurate, and integrated decision-making and implementation of immunization programs at both regional and national levels. The UI/UX design was developed using the User-Centered Design (UCD) method, which actively involves users throughout the process. Usability testing was conducted using the System Usability Scale (SUS) method. The results indicate that the designed UI/UX meets the standard requirements of public health centers and achieves satisfactory outcomes from prospective users. The contribution of this research is the proposed UI/UX design for immunization monitoring. In the future, this UI/UX design can serve as a foundation for the development of an Immunization Monitoring Information System.
Geospatial Analysis of Stunting: A QGIS-based Case Study in Jember Regency Ardianto, Efri Tri; Elisanti, Alinea Dwi; Rusdiarti, Rusdiarti; Pratama, Tegar Wahyu Yudha; Rauf, Muhammad Abdul
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6684

Abstract

Malnutrition, particularly stunting, remains a significant public health concern in Indonesia. In Jember Regency, the prevalence of stunting in 2024 reached 11.4%, which is notably higher than the East Java provincial average of 5.1%. Despite various intervention efforts, the reduction in stunting cases has not been significant, largely due to the lack of evidence-based approaches. This study aimed to analyze the spatial distribution of stunting cases using a geospatial approach. Data were obtained from the electronic Community-Based Nutrition Recording and Reporting System (e-PPGBM) of the Jember District Health Office for the years 2024–2025. The unit of analysis was 31 sub-districts in Jember Regency. Spatial analysis was conducted using Quantum GIS (QGIS) version 3.40.2. In 2024, 58% sub-districts were categorized as very high prevalence, 19.4% sub-districts as high, 19.4% sub-districts as moderate, and 3.2% sub-districts as low. In 2025, the distribution shifted, with 32.3% sub-districts categorized as very high, 25.8% sub-districts as high, 25.8% sub-districts as moderate, and 16.1% sub-districts as low. Trend analysis revealed that 12.9% sub-districts experienced an increase in stunting cases, 41.9% sub-districts remained unchanged, and 45.2% sub-districts showed a decline. Regarding the spatial relationship between regional distance and stunting incidence, urban and coastal areas tend to have fewer cases than highlands areas. Research findings indicate that this is due to limited access to animal protein sources, particularly seafood. Most residents rely on their own livestock products, such as eggs, chicken, and others. Prioritizing interventions in areas with high and very high prevalence rates while strengthening programs to sustain regions with low stunting cases, supported by enhanced geospatial analysis in nutritional surveillance to enable more effective stunting reduction strategies.
Intelligent System for Early Detection of Heart Disease Using XGBoost Machine Learning Algorithm on Web Application Sakkinah, Intan Sulistyaningrum; Hastuti, Puji; Fikri, Muhammad Ainul; Rahmawati, Ulfa Emi; Pratama, Raditya Arief; Firdausya, Maulana Akbar; Anggraini, Ratna Indah
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6685

Abstract

Heart disease remains a major contributor to global mortality, highlighting the importance of effective early detection systems that can assist both clinicians and general users. This study develops a heart disease prediction model based on the XGBoost algorithm and deploys it within a web-based application to enhance accessibility and practical usability. The research uses a dataset of 918 instances containing 12 demographic and clinical features commonly associated with cardiovascular risk. Pearson correlation analysis was performed to assess feature relevance, revealing that ExerciseAngina, Oldpeak, ST_Slope, Age, and MaxHR exhibit the strongest correlations with the HeartDisease outcome. These findings align with established clinical evidence on exercise-induced angina, ST-segment depression, and cardiac functional capacity. Following preprocessing and feature encoding, the XGBoost model was trained and evaluated. The model achieved strong predictive performance, with 88.26% accuracy, 88.32% precision, 91.66% recall, an F1-score of 89.96%, and an ROC-AUC of 0.93. The results demonstrate that XGBoost effectively discriminates between positive and negative cases and provides a good balance between sensitivity and precision. To enable real-world applicability, the final model was deployed on a Flask backend and integrated into a web application that allows users to input clinical parameters and receive real-time predictions. System testing confirmed that the application accurately delivers outputs and functions reliably across different input conditions. Overall, this study shows the feasibility of combining machine learning with web technologies to support early, accessible heart disease screening. Future work will involve usability testing and validation using real patient data to further strengthen the system’s clinical relevance.
Optimizing E-Posyandu Through Digitalization of the Poedji Rochjati Score Card (KSPR) in Detecting the Risk of Maternal Rachmawati, Ervina; Santi, Maya Weka; Puspitasari, Trismayanti Dwi; Yuanta, Yohan
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6695

Abstract

Jember Regency has a high maternal mortality rate (MMR) in East Java Province. Meanwhile, Arjasa Village, Jember is a partner village of Jember State Polytechnic which has the same health problem. In previous research, the e-posyandu application had not developed a system that could detect high-risk pregnant women. The purpose of this study was to optimize E-Posyandu through the Digitalization of the Poedji Rochjati Score Card (KSPR) in Detecting the Risk of Maternal in Arjasa, Jember. Methods: This was a research and development study. The sample were 15 cadres from integrated service posts in Arjasa village. The system was developed using the waterfall method. The results showed that the system’s development of e-Posyandu through the digitalization of the Poedji Rochjati Score Card (KSPR) is able to detect early high-risk pregnant women with three categories: low-risk pregnancy (KRR), high-risk pregnancy (KRT) and very high-risk pregnancy (KRST). High-risk and very high-risk pregnant women are then reported to the village midwife and the Community Health Center to get a referral to the hospital to prevent maternal mortality. Conclusion: This system has been tested with a system accuracy result of 93%, response time of <10 seconds, and user satisfaction with this system reached 87%, so it can increase the efficiency of cadres and midwives in reporting the results of early detection of high-risk pregnant women in real time to the Community Health Center. Recommendation: improve the system by shortening response time, increasing user satisfaction and increase sample size all Posyandu in Jember Regency for future research.
Development of VANESA (Virtual Assistant Nutritional Care Centre for Education and Consultation) for Diabetes Mellitus Management Nuraini, Novita; Wijayanti, Rossalina Adi; Dewi, Riskha Dora Candra; Wicaksono, Andri Permana
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6706

Abstract

Diabetes Mellitus (DM) remains a significant public health challenge in Indonesia, with rising prevalence and limited access to personalized nutritional care. The Nutrition Care Centre (NCC) at Jember State Polytechnic offers diet consultation and education services but faces constraints in human resources and client reach, with only 23% of visitation targets met since 2021. To address this gap, this study aims to develop VANESA, a virtual assistant powered by artificial intelligence (AI) and QR code technology to provide accessible, efficient, and contextual education and consultation services for DM patients. The system integrates rule-based and AI-based chatbot functionalities to deliver natural language responses regarding DM management, including dietary guidance, physical activity, and basic treatment advice. It also facilitates telehealth consultations, automated registration via QR code, and seamless integration with the existing Electronic Medical Record (EMR) system. Developed using the Waterfall model of SDLC, VANESA is designed to enhance service accessibility, reduce operational costs, and support continuous monitoring of patient nutritional status. Expected outcomes include a web-based admin system, an AI-driven Q&A chatbot, telehealth features, QR-based registration, and an analytics dashboard. This innovation not only supports the Teaching Factory NCC but also serves as a scalable model for digital health interventions in resource-limited settings. Thus, VANESA is a digital health solution that has a direct and applicable impact, is able to increase service efficiency, expand the reach of nutritional interventions, and can be replicated and scaled sustainably in various health facilities, especially in areas with limited resources.
Developing Educational Animation on Food Additives for Young Learners Using the 4D Model Kartika, Ria Chandra; Susindra, Yoswenita; Setyowati, Lisus; Satya, Malinda Capri Nurul
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6714

Abstract

Modern dietary patterns have increased children’s consumption of processed foods containing food additives, namely preservatives, flavor enhancers, sweeteners, and colorants (4Ps), which may increase the risk of degenerative diseases in the future. Although many studies discuss unhealthy food consumption among children, limited educational media specifically address food additives in a form that is engaging and suitable for elementary school students. This study aimed to fill this gap by developing an animated educational video on 4P foods as an innovative medium for school-based health promotion. This study used a Research and Development (R&D) approach with the 4D model (Define, Design, Develop, Disseminate) and applied a pre-test–post-test design to evaluate its effectiveness. The developed animation was validated by media and material experts and then tested on 23 elementary school students to assess feasibility, acceptance, and knowledge improvement. The results showed that the animation was highly feasible and well accepted by students. Students reported that the video was interesting, easy to understand, and helpful in learning about healthy food choices. After watching the video, students’ knowledge about food additives increased compared to before the intervention, indicating that the animation effectively supported learning. This study demonstrates that animated educational media can be an effective and practical tool for school health promotion, helping teachers deliver health messages in an engaging way and supporting early prevention of unhealthy eating habits among children.
Web Platform for Automated Detection of Abnormal Red Blood Cells Using Computer Vision Hasanah, Qonitatul; Fitri, Zilvanhisna Emka; Phoa, Victor; Sari, Dian Kartika
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6718

Abstract

Accurate identification of red blood cell (RBC) morphological abnormalities is essential for anemia screening and hematological assessment; however, manual microscopic examination remains time-consuming, subjective, and highly dependent on expert availability. While recent deep learning studies have demonstrated promising accuracy in RBC classification, many focus primarily on model performance without addressing practical deployment constraints or system-level integration for routine laboratory use. In this study, a web-based prototype system for automated RBC abnormality classification is proposed using a lightweight MobileNetV2 architecture. The dataset consisted of 1,320 microscopic blood smear images collected from Klinik & Laboratorium Parahita in Jember and Surabaya, covering six RBC categories with balanced class distribution. All images were anonymized and verified by a certified clinical pathologist prior to use. The model was trained using transfer learning and evaluated on a held-out test set to assess generalization performance. The proposed model achieved a test accuracy of 89.77%, with consistent precision, recall, and F1-score across classes, indicating reliable multi-class classification performance. Analysis of misclassified samples revealed uncertainty primarily between morphologically similar RBC types, reflected by lower confidence scores. These results demonstrate that lightweight deep learning models can provide effective and efficient support for RBC morphology analysis when integrated into an accessible web-based system. The proposed approach contributes a deployment-oriented diagnostic support tool that has the potential to assist laboratory professionals by improving screening efficiency and consistency while preserving clinical oversight.

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