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All Journal Jurnal Ilmu Komputer, Teknologi Dan Informasi
Anisa Rizki Septia
Universitas Muhammadiyah Jambi, Kota Jambi
Computer Science & IT Control & Systems Engineering Engineering Social Sciences Other

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Studi Literatur Review Penerapan Data Mining Untuk Prediksi Penyakit Jantung Menggunakan Naïve Bayes Anisa Rizki Septia; Hetty Rohayani
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 1 (2026): Januari 2026
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i1.268

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.
Co-Authors Hetty Rohayani