Pengelompokan Level Hipertensi Berbasis Tekanan Darah Menggunakan Algoritma K-Means Clustering
DOI:
https://doi.org/10.52436/1.jpti.1477Kata Kunci:
Clustering, Hipertensi, K-Means, Pembelajaran Mesin, Tekanan DarahAbstrak
Hipertensi merupakan salah satu komponen utama sindrom metabolik yang berkontribusi signifikan terhadap peningkatan risiko penyakit kardiovaskular. Deteksi dini dan pemetaan tingkat hipertensi menjadi penting untuk mendukung intervensi medis yang tepat. Penelitian ini bertujuan untuk menerapkan algoritma unsupervised learning Algoritma K-Means Clustering dalam mengelompokkan individu berdasarkan parameter tekanan darah sistolik dan diastolik. Dataset yang digunakan terdiri dari 1.878 catatan pasien, yang setelah proses pembersihan data menghasilkan 1.575 data unik. Data distandarisasi menggunakan StandardScaler, dan jumlah klaster optimal ditentukan melalui metode Elbow. Hasil klasterisasi menunjukkan empat klaster utama yang merepresentasikan segmentasi alami tekanan darah, mulai dari tekanan darah rendah hingga tinggi. Visualisasi dua dimensi dan reduksi dimensi menggunakan Principal Component Analysis (PCA) memperlihatkan pemisahan klaster yang relatif jelas. Temuan ini menunjukkan bahwa K-Means mampu mengidentifikasi struktur laten data tekanan darah secara objektif dan berpotensi menjadi dasar pengembangan sistem pendukung keputusan medis berbasis data untuk stratifikasi risiko hipertensi.
Unduhan
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