Studi Literatur Review Penerapan Data Mining Untuk Prediksi Penyakit Jantung Menggunakan Naïve Bayes

Anisa Rizki Septia; Hetty Rohayani

doi:10.62866/jurikti.v4i1.268

Anisa Rizki Septia * Universitas Muhammadiyah Jambi, Kota Jambi, Indonesia
Hetty Rohayani Universitas Muhammadiyah Jambi, Kota Jambi, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.62866/jurikti.v4i1.268

Keywords: Heart Disease; Data Mining; Naïve Bayes; Disease Predictions; Early Detection

Abstract

Heart disease is one of the leading causes of global death, often difficult to detect early due to non-specific clinical symptoms. To overcome the limitations of manual diagnosis, the application of data mining techniques utilizing the Naïve Bayes algorithm presents an efficient and accurate computational solution. This study aims to analyze and map the effectiveness of Naïve Bayes implementation in predicting heart disease through a Systematic Literature Review (SLR) approach. The contribution of this study is to provide a comprehensive taxonomic guide regarding the influence of data geometry, preprocessing techniques, and the integration of feature selection methods on optimizing the performance of probabilistic models. The results of the literature review indicate that the model accuracy level varies between 58% and 91.80%, with the majority of performance stable in the range of 79%-91% which is deterministically influenced by the quality of data dimensionality reduction. Overall, the Naïve Bayes-based data mining process has proven to have great potential as a clinical decision support system in supporting early medical preventive measures.

References

S. Sahar, “Analisis Perbandingan Metode K-Nearest Neighbor dan Naïve Bayes Clasiffier Pada Dataset Penyakit Jantung,” Indones. J. Data Sci., vol. 1, no. 3, pp. 79–86, 2020, doi: 10.33096/ijodas.v1i3.20.

Khodijah and Sriyanto, “Perbandingan Kinerja Algoritma C4.5. Naive Bayes Dan Random Forest Dalam Prediksi Penyakit Jantung,” J. Tek., vol. 17, no. 2, pp. 419–426, 2023.

P. A. Sihotang and D. Sitanggang, “Penerapan Metode Algoritma C4.5 Dan Naive Bayes Untuk Prediksi Penyakit Jantung,” J. Tek. Inf. dan Komput., vol. 7, no. 2, p. 899, 2024, doi: 10.37600/tekinkom.v7i2.1535.

S. M. Mellisa, S. Sagarung, and H. Parmadi, “Pendekatan correlated naïve bayes pada klasifikasi potensi penyakit jantung,” Pros. Semin. Nas. Penelit. dan Pengabdi. Kpd. Masy. LPPM Univ. ’Aisyiyah Yogyakarta, vol. 1, pp. 22–2023, 2023, [Online]. Available: https://www.kaggle.com/datasets/firdaus9914/penyakit-jantung.

B. Hirwono, A. Hermawan, and D. Avianto, “Implementasi Metode Naïve Bayes untuk Klasifikasi Penderita Penyakit Jantung,” J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), vol. 7, no. 3, pp. 450–457, 2023, doi: 10.35870/jtik.v7i3.910.

S. R. Azizah, R. Herteno, A. Farmadi, D. Kartini, and I. Budiman, “Kombinasi Seleksi Fitur Berbasis Filter dan Wrapper Menggunakan Naive Bayes pada Klasifikasi Penyakit Jantung,” J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 6, pp. 1361–1368, 2023, doi: 10.25126/jtiik.2023107467.

A. Lutfia, G. Gunawan, R. S. Rohman, and A. Gunawan, “Penerapan Seleksi Fitur Gain Ratio Pada Prediksi Penyakit Jantung Berbasis Naïve Bayes,” J. Responsif Ris. Sains dan Inform., vol. 6, no. 1, pp. 1–10, 2024, doi: 10.51977/jti.v6i1.1396.

A. Nurfazila and H. Rohayani, “Literature Review : Implementation of the Naive Bayes Algorithm for Classification in Various Fields of Data Mining,” vol. 3, no. 1, pp. 240–253, 2026.doi:https://doi.org/10.61098/ijiretm.v3i1.269

H. Rohayani, S. N. Alam, M. Fauzi, and R. Rico, “Prediksi Penyebaran Virus COVID-19 Dari Hasil PCR Menggunakan Metode Naïve Bayes,” J. Comput. Syst. Informatics, vol. 4, no. 1, pp. 109–115, 2022, doi: 10.47065/josyc.v4i1.2577.

S. R. Fernanda and P. D. Mardika, “Implementasi Data Mining untuk Mendiagnosa Penyakit Jantung dengan Algoritma Naïve Bayes di RS TK II Moh. Ridwan Mauraksa,” Semnas Ristek (Seminar Nas. Ris. dan Inov. Teknol., vol. 9, no. 1, pp. 114–118, 2025, doi: 10.30998/semnasristek.v9i1.7779.

A. Suko Wijoyo and A. Jananto, “Rancang Bangun Sistem Informasi Prediksi Penyakit Jantung Berbasis Algoritma Naive Bayes,” J. Tek. Inform. Unika ST. Thomas, vol. 8, no. 2, pp. 181–190, 2023.

A. Atthohiroh, R. Ayu, and S. Maharani, “Penerapan Metode Naive Bayes Untuk Memprediksi Penyakit Jantung,” J. Tek., vol. 3, no. 1, p. 8, 2023, doi: 10.54314/teknisi.v3i1.1252.

A. N. Am, M. Nurkholifah, and F. K. Oktorina, “Analisa Penyakit Jantung Menggunakan Algoritma Naïve Bayes,” J. Syst. Comput. Eng., vol. 4, no. 1, pp. 26–36, 2023, doi: 10.47650/jsce.v4i1.671.

R. M. Ubaidilah and T. A. Puspito, “Optimasi Prediksi Penyakit Jantung Dengan Naïve Bayes Dan Particle Swarm Optimization (Pso),” J. Inf. dan Komput., vol. 13, no. 1, p. 2025, 2025, [Online]. Available: www.kaggle.com

I. Alhabib, “Komparasi Metode Deep Learning, Naïve Bayes Dan Random Forest Untuk Prediksi Penyakit Jantung,” INFORMATICS Educ. Prof. J. Informatics, vol. 6, no. 2, p. 176, 2022, doi: 10.51211/itbi.v6i2.1881.

Oskar, Fransiska Ria, Rupina, and N. P, “Pengelolaan Data Penyakit Jantung Dengan Menggunakan Metode Naive Bayes,” J. Sains Dan Komput., vol. 8, no. 02, pp. 49–54, 2024, doi: 10.61179/jurnalinfact.v8i02.531.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Studi Literatur Review Penerapan Data Mining Untuk Prediksi Penyakit Jantung Menggunakan Naïve Bayes