Implementasi Algoritma DBSCAN Dalam Mengelompokan Data Pasien Terdiagnosa Penyakit Ginjal Kronis(PGK)

  • Richardo Anggara MDP
  • Abdul Rahman Universitas Multi Data Palembang
Keywords: Chronic kidney disease, DBSCAN, Epsilon, Cluster

Abstract

Chronic disease (CKD) is a kidney disease characterized by structural or functional kidney damage that lasts more than three months. CKD is characterized by one or more signs of kidney damage, namely albuminuria, abnormal urine sediment, electrolytes, histology, renal structure, or a history of kidney transplantation, with decreased glomerular filtration rate. This study used the DBSCAN implementation method to classify data on diagnosed CKD patients. The essential data used is Chronic Kidney Disease which collects 400 medical data with 24 attributes or features. The study's results note many clusters and noise obtained using Euclidean metrics. The best results are in the second scenario with an epsilon value of 3.5 and Min sample = 5, which produces a total of 2 clusters with a Silhouette value of 0.158.

Published
2022-10-10
How to Cite
Anggara, R., & Rahman, A. (2022, October 10). Implementasi Algoritma DBSCAN Dalam Mengelompokan Data Pasien Terdiagnosa Penyakit Ginjal Kronis(PGK). Jurnal Algoritme, 3(1), 114-123. https://doi.org/https://doi.org/10.35957/algoritme.v3i1.3593