PENGELOMPOKAN DATA PERSEDIAAN OBAT MENGGUNAKAN PERBANDINGAN METODE KMEANS DENGAN HIERARCHICAL CLUSTERING SINGLE LINKAGE
Total View This Week0
Total View This Week0
Institusion
Institut Teknologi Telkom Purwokerto
Author
RAHMATIKA, DIANA FIRDAUS
Subject
T Technology (General)
Datestamp
2021-04-26 03:05:17
Abstract :
ABSTRACT One important factor in the world of health is the availability of medicines to be distributed throughout Indonesia, through an evenly and sustainable governmentowned health organization body. The grouping of drug supplies in the Pukesmas II Ajibarang is still done manually, causing errors in theresults output obtained. Utilization of data mining in its development is able to process and group large amounts of data based on similarities in a set of data, can be a solution to the above problems.algorithm K-Means is a grouping method is simple and easy to use while Hierarchical Clustering (HCC) Single Linkage to the determination of the center point of the cluster(centroid) have consistent properties and complex. The use of the R programming language is able to offer many statistical and graphic choices. 204 data and 5 variables will be processed, the two algorithms will get optimal clusters according to the similarity of data in cluster C1, namely drugs with slow use and cluster C2, namely drugs with rapid use and comparing the value of validity. The results of this study indicate that the HCC Single able to provide the best results with Sillhoutte Index (SI) Linkage algorithm isvalidity of 0.8629 while the K-Means algorithm gets thevalidity value SI of 0.8414. The LPLPO form grouping is expected to be able to help get output the appropriate. Keywords: Algorithms, Data Mining, Hierarchical Clustering, K-Means, Medicine, Sillhoutte Index.
ABSTRACT One important factor in the world of health is the availability of medicines to be distributed throughout Indonesia, through an evenly and sustainable governmentowned health organization body. The grouping of drug supplies in the Pukesmas II Ajibarang is still done manually, causing errors in theresults output obtained. Utilization of data mining in its development is able to process and group large amounts of data based on similarities in a set of data, can be a solution to the above problems.algorithm K-Means is a grouping method is simple and easy to use while Hierarchical Clustering (HCC) Single Linkage to the determination of the center point of the cluster(centroid) have consistent properties and complex. The use of the R programming language is able to offer many statistical and graphic choices. 204 data and 5 variables will be processed, the two algorithms will get optimal clusters according to the similarity of data in cluster C1, namely drugs with slow use and cluster C2, namely drugs with rapid use and comparing the value of validity. The results of this study indicate that the HCC Single able to provide the best results with Sillhoutte Index (SI) Linkage algorithm isvalidity of 0.8629 while the K-Means algorithm gets thevalidity value SI of 0.8414. The LPLPO form grouping is expected to be able to help get output the appropriate. Keywords: Algorithms, Data Mining, Hierarchical Clustering, K-Means, Medicine, Sillhoutte Index.
Download
book
BibTex
Latex, Jabref
cloud_download
Original Resource
url resource Institution
