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

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.