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All Journal Rekursif: Jurnal Informatika
Lady, Nehemia Artah Sasta
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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IDENTIFIKASI HIPERTENSI DARI DATA GENOMIC MENGGUNAKAN HYBRID CNN-K MEAN CLUSTERING Lady, Nehemia Artah Sasta; Ernawati, Ernawati; Sari, Julia Purnama
Rekursif: Jurnal Informatika Vol 14 No 1 (2026): Volume 14 Nomor 1 Maret 2026
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v14i1.47435

Abstract

Hypertension is a degenerative disease that ranks as a leading cause of death worldwide. Early detection and accurate classification of hypertension patients are crucial for appropriate and effective treatment. Genetic factors contribute to the risk of hypertension in 30–60% of individuals. Multifactorial and asymptomatic hypertension complicates detection and prediction, necessitating the development of a Hybrid CNN K-Mean Clustering model to predict hypertension. This research method uses a hybrid CNN and K-Means Clustering to analyze genomic data in the form of Single Nucleotide Polymorphisms (SNPs). The results showed 100% classification accuracy with evaluation metrics such as 100% precision, 100% recall, and 100% F1-score, indicating the model's excellent ability to recognize and classify results.
Co-Authors Ernawati Ernawati Julia Purnama Sari