%0 Thesis
%9 Skripsi
%A ROSALIA SUSILOWATI, NIM. 08650080
%B FAKULTAS SAINS DAN TEKNOLOGI
%D 2012
%F digilib:8193
%I UIN SUNAN KALIJAGA
%T CLUSTERING DATA PASIEN MENGGUNAKAN  FUZZY C-MEANS DAN   AGGLOMERATIVE HIERARCHICAL CLUSTERING
%U https://digilib.uin-suka.ac.id/id/eprint/8193/
%X During these days, system development only includes input, view and  report in general. From the medical record database at Rumah Sakit Jogja,  informations which can be obtained just the percentage of male and female  patient, the more specific information which can be retrieved has not been done.  With the completeness of informations from the database, so that can used as  consideration of decision making by the authorities. These informations can be  obtained from analysis and processing available data, one of methods for  retrieving information is clustering technique.     Domain data of this research is patient data. Before the data clustered, first  performed prepocessing which includes naming standardization, changing form or  data numerisation and normalization. During the clustering process, the used  algorithms are Fuzzy C-Means (FCM) and Agglomerative Hierarchical  Clustering(AHC). The purpose of the use of these two algorithms is to determine  the most appropriate algorithm and the faster processing time for patient data  cases.     Processing time required to perform clustering using FCM Algorithm  relatively faster than AHC algorithm. For small volume data, FCM algorithm's  iteration is more than AHC algorithm, however, the clustering result of FCM  algorithm is easier to interpreted than AHC algorithm. If seen from the clustering  result visualization, cluster pattern of FCM algorithm is more clustered based on  three variables. So, for medical records as domain of this research, the more  precise algorithm is the Fuzzy C-Means (FCM) algorithm.    Keywords: Agglomerative Hierarchical Clustering,  Clustering, Fuzzy C-Means,Patient Data