MODIFIKASI ALGORITMA FUZZY C-MEANS CLUSTERING (Studi Kasus : Pengelompokkan Propinsi Berdasarkan Kualitas Pendidikan Madrasah)

• Main Authors: , Muhammad Fajeri, S. Pd, , Prof. Drs. H. Subanar, Ph.D
• Format: Theses and Dissertations NonPeerReviewed
• Published: [Yogyakarta] : Universitas Gadjah Mada 2011
• Subjects: ETD
• Online Access: https://repository.ugm.ac.id/97380/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=53754
• Institution: Universitas Gadjah Mada

Fuzzy C-Means (FCM) is a data clustering technique where the existence of each data point in a cluster is determined by the degree of membership that is on the interval [0,1]. One of the deficiencies that exist in the classical FCM method is that the membership of a data value to a particular cluster depends directly to the membership value of the data on another cluster, this is caused by the constraint functions it has. that Several new algorithms are developed to improve the performance of the FCM, including the Adaptive Fuzzy Clustering (FAC) and the Modified Fuzzy C- Means (MFCM). Meanwhile, a measuring tool used to evaluate the performance of clustering methods is to use the ratio of standard deviation in the group and the standard deviation between groups. Based on the results of grouping by using the data quality of madrasa education, it turns out MFCM method has better performance when compared with the other two methods