PENGELOMPOKAN DATA PERSEDIAAN OBAT MENGGUNAKAN PERBANDINGAN METODE KMEANS DENGAN HIERARCHICAL CLUSTERING SINGLE LINKAGE

RAHMATIKA, DIANA FIRDAUS (2019) PENGELOMPOKAN DATA PERSEDIAAN OBAT MENGGUNAKAN PERBANDINGAN METODE KMEANS DENGAN HIERARCHICAL CLUSTERING SINGLE LINKAGE. Undergraduate Thesis thesis, Institut Telkom Purwokerto.

	Text COVER.pdf - Accepted Version Download (1MB)
Preview	Text ABSTRACT.pdf - Accepted Version Download (107kB) | Preview
Preview	Text ABSTRAK.pdf - Accepted Version Download (8kB) | Preview
Preview	Text BAB I.pdf - Accepted Version Download (232kB) | Preview
Preview	Text BAB II.pdf - Accepted Version Download (311kB) | Preview
Preview	Text BAB III.pdf - Accepted Version Download (544kB) | Preview
	Text BAB IV.pdf - Accepted Version Restricted to Registered users only Download (243kB)
Preview	Text BAB V.pdf - Accepted Version Download (107kB) | Preview
Preview	Text DAFTAR PUSTAKA.pdf - Accepted Version Download (224kB) | Preview

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.

Actions (login required)

View Item