Penerapan Algoritma Partitioning Around Medoids Untuk Menentukan Kelompok Penyakit Pasien (Studi Kasus : Puskesmas Kajen Pekalongan) - Anindya Khrisna Wardhani (KILAT, 2017)
Heaps of data residing on health services, polyclinics, hospitals and clinics are now only limited to providing graphs or statistics on the number of patients seeking treatment. The contents of the report in the form of the illness and its report medicine information from the disease.This research applies methods of partitioning around medoids (k-medoids) to produce information about the grouping of the disease "Acute" and "NOT ACUTE" that affects many patients in Puskesmas Kajen Pekalongan. Then these results can be used as ingredients or basic health education by the local Health Department. Based on the data obtained, the resulting number of acute cluster there are 94 items, not acute cluster 906 items with a total amount of data is 1000.