Work

Penggunaan K-means Dan Hierarchical Clustering Single Linkage Dalam Pengelompokkan Stok Obat

Year: 2024

Type: article

Abstract: Adequate and efficient availability of medicines is necessary to ensure patients receive optimal care. However, inefficient drug stock management can result in various problems, such as waste of resources, lack of necessary drugs, or even excessive stock. This study aims to improve the efficiency of the drug stock management process by using KMeans Clustering and Hierarchical Clustering methods on drug stock data. The data used includes information on initial stock, purchase, incoming distribution, service, outgoing distribution, outgoing adjustment, and final stock. Clustering analysis was performed to identify patterns in the drug stock data, which was then validated using Silhouette Score. The results showed that Hierarchical Clustering was able to achieve a Silhouette Score of 0.976, while KMeans achieved a Silhouette Score of 0.954

Source: Jurnal Lebesgue Jurnal Ilmiah Pendidikan Matematika Matematika dan Statistika

DOI: 10.46306/lb.v5i2.715

Authors: Michael Alexander Justin Audison Sibarani, I Gede Susrama Mas Diyasa, Sugiarto Sugiarto

Institution: University of Pembangunan Nasional Veteran Jawa Timur

Language: English

Topic: Data Mining and Machine Learning Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $16

OpenAlex ID: W4401944076

FWCI: 0.774

Cites:

Cited by: 2

Related to: 10