cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Muhammad Yunus
Contact Email
m.yunus@polije.ac.id
Phone
+6288803680040
Journal Mail Official
ijhitech@polije.ac.id
Editorial Address
Jl. Mastrip PO Box 164, Jember, Jawa Timur
Location
Kab. jember,
Jawa timur
INDONESIA
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 2 Documents
 1 
Search results for , issue "Vol. 3 No. 2 (2026): January (In Progress)" : 2 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 (In Progress)
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 (In Progress)
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.

Page 1 of 1 | Total Record : 2

 1 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol. 3 No. 2 (2026): January (In Progress) Vol. 3 No. 1 (2025): July Vol. 2 No. 2 (2025): January Vol. 2 No. 1 (2024): July Vol. 1 No. 2 (2024): January Vol. 1 No. 1 (2023): July