Pengelompokkan Data Persediaan Obat Menggunakan Metode K-Means dan Hierarchical Clustering

Sibarani, Michael Alexander Justin Audison (2024) Pengelompokkan Data Persediaan Obat Menggunakan Metode K-Means dan Hierarchical Clustering. Undergraduate thesis, UPN Veteran Jawa Timur.

	Text 20083010019-Cover.pdf Download (1MB)
	Text (BAB I) 20083010019-bab 1.pdf Download (195kB)
	Text (BAB II) 20083010019-bab 2.pdf Restricted to Repository staff only until 30 July 2026. Download (574kB)
	Text (BAB III) 20083010019-bab 3.pdf Restricted to Repository staff only until 30 July 2026. Download (211kB)
	Text (BAB IV) 20083010019-bab 4.pdf Restricted to Repository staff only until 30 July 2026. Download (397kB)
	Text (BAB V) 20083010019-bab 5.pdf Download (186kB)
	Text (Daftar Pustaka) 20083010019-daftarpustaka.pdf Download (303kB)
	Text (LAMPIRAN) 20083010019-lampiran.pdf Restricted to Repository staff only Download (446kB)

Abstract

Efficient drug availability is essential to ensure patients receive optimal care. At RS Royal Taruma Jakarta, inefficient drug stock management leads to wasted resources, drug shortages, or overstocks. This study aims to improve the efficiency of drug stock purchases at Royal Taruma Hospital Jakarta using the Hierarchical Clustering and K-Means Clustering methods. The drug stock data used includes initial stock, purchase, incoming distribution, service, outgoing distribution, outgoing adjustment, and final stock. The data was collected from the hospital management system and standardized for analysis. Hierarchical Clustering was used to understand the data structure, while K-Means Clustering was used to group the data based on the centroid. The results showed that Hierarchical Clustering with three clusters had a silhouette score of 0.976, while K-Means Clustering with three clusters had a silhouette score of 0.954. Based on these results, Royal Taruma Hospital Jakarta can implement a more efficient drug stock management strategy, ensuring sufficient drug availability and avoiding waste.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Diyasa, I Gede Susrama Mas	NIDN0019067008	igsusrama.if@upnjatim.ac.id
Thesis advisor	Sugiarto, Sugiarto	NIDN19870214202121100	sugiarto.if@upnjatim.com

Subjects:

Q Science > QA Mathematics > QA76.6 Computer Programming

Divisions:

Faculty of Computer Science > Departemen of Data Science

Depositing User:

Michael Alexander Justin Audison Alexander

Date Deposited:

31 Jul 2024 07:42

Last Modified:

31 Jul 2024 07:42

URI:

https://repository.upnjatim.ac.id/id/eprint/28021