Yhenis Apriliana
Department of Mathematics, Jenderal Soedirman University

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

APLIKASI K-MEANS DAN FUZY CLUSTERING DALAM PENGELOMPOKAN KECAMATAN DI KABUPATEN BANYUMAS jajang Jajang; Nunung Nurhayati; Yhenis Apriliana
Jurnal Ilmiah Matematika dan Pendidikan Matematika Vol 13 No 2 (2021): Jurnal Ilmiah Matematika dan Pendidikan Matematika
Publisher : Jurusan Matematika FMIPA Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jmp.2021.13.2.5051

Abstract

Clustering N objects into c clusters can be used to get information about data observation. Among the clustering methods are K-Means (KMC) and Fuzzy C-means (FCM) methods. In the K-means method, objects are members or not members of the cluster, while in the FCM method, objects are included in the cluster based on the degree of membership. This study discusses the implementation of KMC and FCM in the custering of sub-districts in Banyumas Regency based on total of population, the number of health workers and the number of health facilities and infrastructure. The results showed that the KMC and FCM methods produced the same cluster membership. Furthermore, the analysis of clustering based on the number of population, the number of health workers and the number of health facilities and infrastructure (scenario 1) and based on the number of health workers and the number of health facilities and infrastructure which have been corrected by population (scenario 2). The percentage of the variance ratio between clusters to the total variance in scenario 1 is 69% while in scenario 2 it is 85%. Clustering based on scenario 2 is better than scenario 1.
APLIKASI K-MEANS DAN FUZY CLUSTERING DALAM PENGELOMPOKAN KECAMATAN DI KABUPATEN BANYUMAS jajang Jajang; Nunung Nurhayati; Yhenis Apriliana
Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Vol 13 No 2 (2021): Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP)
Publisher : Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jmp.2021.13.2.5051

Abstract

Clustering N objects into c clusters can be used to get information about data observation. Among the clustering methods are K-Means (KMC) and Fuzzy C-means (FCM) methods. In the K-means method, objects are members or not members of the cluster, while in the FCM method, objects are included in the cluster based on the degree of membership. This study discusses the implementation of KMC and FCM in the custering of sub-districts in Banyumas Regency based on total of population, the number of health workers and the number of health facilities and infrastructure. The results showed that the KMC and FCM methods produced the same cluster membership. Furthermore, the analysis of clustering based on the number of population, the number of health workers and the number of health facilities and infrastructure (scenario 1) and based on the number of health workers and the number of health facilities and infrastructure which have been corrected by population (scenario 2). The percentage of the variance ratio between clusters to the total variance in scenario 1 is 69% while in scenario 2 it is 85%. Clustering based on scenario 2 is better than scenario 1.