Published: 2023-07-01

Implementasi Metode Naïve Bayes untuk Klasifikasi Penderita Penyakit Jantung

DOI: 10.35870/jtik.v7i3.910

Bowo Hirwono, Arief Hermawan, Donny Avianto
  • Bowo Hirwono: Universitas Teknologi Yogyakarta ROR
  • Arief Hermawan: Universitas Teknologi Yogyakarta ROR
  • Donny Avianto: Universitas Teknologi Yogyakarta ROR
Issue Cover
Article Metrics
Share:

Abstract

Heart attack is a very serious heart disorder. This disorder occurs when the heart muscle does not get good blood flow. This condition will interfere with the function of the heart in flowing blood flow throughout the body. This study aims to develop a system capable of classifying people with heart disease using the Naïve Bayes method. Naïve Bayes is a method that works based on the probability that a person has a heart disease or not based on their medical record data. This algorithm is used with the aim of calculating the probability of a person suffering from heart disease based on their medical records. This data was obtained from the University of California Irvine Machine Learning website with a total of 303 datasets with 13 attributes. This research was conducted by dividing the data into 75% for training data and 25% for training data. The results of this study indicate that the Naïve Bayes algorithm used gives a fairly high accuracy value of 86.84%.

Keywords

Naïve Bayes; Heart Disease; Classification

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.

Open Science Badges

This journal supports transparency in research and encourages authors to meet criteria for Open Science Badges.

How to Cite

Hirwono, B., Hermawan, A., & Avianto, D. (2023). Implementasi Metode Naïve Bayes untuk Klasifikasi Penderita Penyakit Jantung. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 7(3), 450-457. https://doi.org/10.35870/jtik.v7i3.910
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. 7 No. 3 (2023)

  • Section: Computer & Communication Science

  • Published: 2023-07-01

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.

Semantic Scholar Scite Dimensions Connected Papers
Scholarly Connection Platforms
Dimensions Dimensions Connected Papers Connected Papers Scite Scite Google Scholar Google Scholar Semantic Scholar Semantic Scholar Garuda Garuda Scilit Scilit Crossref Crossref BASE BASE Zenodo Zenodo Unpaywall Unpaywall OpenCitations OpenCitations
Author Biographies
Bowo Hirwono, Universitas Teknologi Yogyakarta
Google Scholar ResearchGate ORCID

Program Studi Informatika, Fakultas Teknik, Universitas Teknologi Yogyakarta, Kabupaten Sleman, Daerah Istimewa Yogyakarta, Indonesia

Arief Hermawan, Universitas Teknologi Yogyakarta
Google Scholar ResearchGate ORCID

Program Studi Magister Teknologi Informasi, Fakultas Teknik, Universitas Teknologi Yogyakarta, Kabupaten Sleman, Daerah Istimewa Yogyakarta, Indonesia

Donny Avianto, Universitas Teknologi Yogyakarta
Google Scholar ResearchGate ORCID

Program Studi Magister Teknologi Informasi, Fakultas Teknik, Universitas Teknologi Yogyakarta, Kabupaten Sleman, Daerah Istimewa Yogyakarta, Indonesia

References
  • Wibisono, A.B. and Fahrurozi, A., 2020. Perbandingan Algoritma Klasifikasi Dalam Pengklasifikasian Data Penyakit Jantung Koroner. jurnal ilmiah teknologi dan rekayasa, 24(3), pp.161-170. DOI: http://dx.doi.org/10.35760/tr.2019.v24i3.2393.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Yudistira, N. and Putra, A.F., 2021. Algoritma Decision Tree Dan Smote Untuk Klasifikasi Serangan Jantung Miokarditis Yang Imbalance. Jurnal Litbang Edusaintech, 2(2), pp.112-122. DOI: https://doi.org/10.51402/jle.v2i2.48.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Alhamad, A., Azis, A.I., Santoso, B. and Taliki, S., 2019. Prediksi Penyakit Jantung Menggunakan Metode-Metode Machine Learning Berbasis Ensemble–Weighted Vote. JEPIN (Jurnal Edukasi dan Penelitian Informatika), 5(3), pp.352-360. DOI: http://dx.doi.org/10.26418/jp.v5i3.37188.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Riani, A., Susianto, Y. and Rahman, N., 2019. Implementasi Data Mining Untuk Memprediksi Penyakit Jantung Mengunakan Metode Naive Bayes. Journal of Innovation Information Technology and Application (JINITA), 1(01), pp.25-34.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Larassati, D., Zaidiah, A. and Afrizal, S., 2022. Sistem prediksi penyakit jantung koroner menggunakan metode naïve bayes. JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), 7(2), pp.533-546. DOI: https://doi.org/10.29100/jipi.v7i2.2842.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Al Hafiz, D.M., Amaly, K., Jonathan, J. and Rachmatullah, M.T., 2021. Sistem Prediksi Penyakit Jantung Menggunakan Metode Naive Bayes. Jurnal Rekayasa Elektro Sriwijaya, 2(2), pp.151-157. DOI: https://doi.org/10.36706/jres.v2i2.29.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Nawawi, H.M., Purnama, J.J. and Hikmah, A.B., 2019. Komparasi Algoritma Neural Network Dan Naïve Bayes Untuk Memprediksi Penyakit Jantung. Jurnal Pilar Nusa Mandiri, 15(2), pp.189-194. DOI: https://doi.org/10.33480/pilar.v15i2.669.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Alhabib, I., 2022. Komparasi Metode Deep Learning, Naïve Bayes dan Random Forest untuk Prediksi Penyakit Jantung. INFORMATICS FOR EDUCATORS AND PROFESSIONAL: Journal of Informatics, 6(2), pp.176-185. DOI: https://doi.org/10.51211/itbi.v6i2.1881.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Bianto, M.A., Kusrini, K. and Sudarmawan, S., 2020. Perancangan Sistem Klasifikasi Penyakit Jantung Mengunakan Naïve Bayes. Creative Information Technology Journal, 6(1), pp.75-83. DOI: https://doi.org/10.24076/citec.2019v6i1.231.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Annisa, R., 2019. Analisis Komparasi Algoritma Klasifikasi Data Mining Untuk Prediksi Penderita Penyakit Jantung. JTIK (Jurnal Teknik Informatika Kaputama), 3(1), pp.22-28.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Rasywir, E., Meisak, D., Pratama, Y. and Feranika, A., 2022. Penerapan Algoritma Naïve Bayes Untuk Mengetahui Pasien Penyakit Gagal Jantung. Jurnal Informatika Dan Rekayasa Komputer (JAKAKOM), 2(2), pp.263-266. DOI: https://doi.org/10.33998/jakakom.2022.2.2.117.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Sahar, S., 2020. Analisis Perbandingan Metode K-Nearest Neighbor dan Naïve Bayes Clasiffier Pada Dataset Penderita Penyakit Jantung. Indonesian Journal of Data and Science, 1(3), pp.79-86. DOI: https://doi.org/10.33096/ijodas.v1i3.20.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Shah, D., Patel, S. and Bharti, S.K., 2020. Heart disease prediction using machine learning techniques. SN Computer Science, 1, pp.1-6. DOI: https://doi.org/10.1007/s42979-020-00365-y.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Munandar, T.A. and Munir, A.Q., 2022. Implementasi K-Nearest Neighbor Untuk Prototype Sistem Pakar Identifikasi Dini Penyakit Jantung. Respati, 17(2), pp.44-50. DOI: https://doi.org/10.35842/jtir.v17i2.457.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Partogi, Y. and Pasaribu, A., 2021. Perancangan Metode Decision Tree Terhadap Sistem Perpustakaan STMIK Kuwera. Jurnal Sistem Informasi dan Teknologi (SINTEK), 1(2), pp.20-25. DOI: https://doi.org/10.56995/sintek.v1i2.4.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
License & Copyright
Creative Commons License

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.

Downloads