Published: 2025-07-01
Expert System for Diagnosis of Hypertension Disease Using Naive Bayes Method
DOI: 10.35870/jtik.v9i3.3822
Edhy Poerwandono, Prakoso Angga Ilyasa
- Edhy Poerwandono: Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika , Affiliation name not available , Indonesia
- Prakoso Angga Ilyasa: Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika , Affiliation name not available , Indonesia
Article Metrics
- Views 0
- Downloads 0
- Scopus Citations
- Google Scholar
- Crossref Citations
- Semantic Scholar
- DataCite Metrics
-
If the link doesn't work, copy the DOI or article title for manual search (API Maintenance).
Abstract
Hypertension is a disorder of the blood vessels that causes the supply of oxygen and nutrients carried by the blood to be blocked to the body's tissues that need it. Hypertension is often referred to as a silent killer, because it is a deadly disease without symptoms as a warning to its victims. Hypertension sufferers range from 40 years of age and above to lifelong. In general, hypertension is caused by hereditary factors, unhealthy lifestyles, excessive salt consumption, alcoholic beverages and stress. Expert systems can be a solution to solve problems because this system works like an expert and is designed using the naive bayes method by looking at the rules and rule bases that exist in hypertension. Through this application, users can consult with the system like consulting an expert to diagnose the symptoms that occur in users and find solutions to the problems faced.
Keywords
Expert System ; Hypertension ; Naive Bayes
Article Metadata
Peer Review Process
This article has undergone a double-blind peer review process to ensure quality and impartiality.
Indexing Information
Discover where this journal is indexed at our indexing page to understand its reach and credibility.
Open Science Badges
This journal supports transparency in research and encourages authors to meet criteria for Open Science Badges by sharing data, materials, or preregistered studies.
How to Cite
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 9 No. 3 (2025)
-
Section: Computer & Communication Science
-
Published: %750 %e, %2025
-
License: CC BY 4.0
-
Copyright: © 2025 Authors
-
DOI: 10.35870/jtik.v9i3.3822
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
Edhy Poerwandono
Informatics Engineering Study Program, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia.
-
-
-
-
-
-
-
-
-
-
Roanes-Lozano, E., López-Vidriero, E., Laita, L. M., López-Vidriero, E., Maojo, V., & Roanes-Macías, E. (2004). An expert system on detection, evaluation and treatment of hypertension. In Artificial Intelligence and Symbolic Computation: 7th International Conference, AISC 2004, Linz, Austria, September 22-24, 2004. Proceedings 7 (pp. 251-264). Springer Berlin Heidelberg.
-
Rokhmah, S., & Rais, N. A. R. (2022). Application of Data Mining for Prediction of Long Covid on Covid-19 Survival With Feature Selection and Naïve Bayes Method. Jurnal Teknik Informatika (Jutif), 3(5), 1397-1405. https://doi.org/10.20884/1.jutif.2022.3.5.561.
-
-

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.