Clustering Pasien Yang Layak Donor Darah Dengan Algoritma K-Means Studi Kasus Pmi Kota Medan

M Rizky Ramadhan (Universitas Budi Darma, Medan)

Jurnal Sains dan Teknologi Informasi
Vol 2 No 3 (2023): Juni 2023

Adequate supply of blood is important in health services, especially in emergency situations. In an effort to increase the efficiency and effectiveness of the selection process for prospective blood donors, this study aims to apply the K-Means algorithm in clustering patients who are eligible to become blood donors at PMI Medan City. Patient data including age, weight, hemoglobin level and blood pressure were obtained from UDD PMI Medan City. The K-Means algorithm is then applied to the data to form groups based on patient characteristics. The results of the study show that the K-Means algorithm is able to classify patients into groups based on their characteristics efficiently, and can provide guidance to PMI Medan City in selecting potential blood donors and improving the collection and distribution of blood supplies in a more targeted manner