PENERAPAN DATA MINING DALAM MENGELOMPOKKAN BENCANA ALAM MENGGUNAKAN ALGORITMA K-MEDOIDS (STUDI KASUS: BADAN PENANGGULANGAN BENCANA DAERAH (BPBD) PROVINSI JAWA TENGAH)

SIDAURUK, IKO GANDA PUTRA and Kurniati, Rizki and Saputra, Danny Matthew (2024) PENERAPAN DATA MINING DALAM MENGELOMPOKKAN BENCANA ALAM MENGGUNAKAN ALGORITMA K-MEDOIDS (STUDI KASUS: BADAN PENANGGULANGAN BENCANA DAERAH (BPBD) PROVINSI JAWA TENGAH). Undergraduate thesis, Sriwijaya University.

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Abstract

Natural disasters are a major problem faced by many regions, including Central Java Province, where natural disasters often result in losses both in terms of casualties and material. This research focuses on the application of the K-Medoids algorithm to analyze data on natural disaster events in Central Java Province. The data analyzed in the time span of 2019 to 2023, which was obtained from the BPBD Office of Central Java Province, and involved data collection, preparation, and preprocessing processes. Data clustering resulted in two clusters for the natural disaster event dataset with a silhouette coefficient evaluation value of 0.50. The optimal number of clusters in the data clustering is 2 clusters, because the silhouette coefficient value is the highest when the value of K = 2. This confirms the effectiveness of the K-Medoids algorithm in determining the right number of clusters in the analysis of natural disaster events.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Silhoutte Coefficient, Bencana Alam, K-Medoids, Klasterisasi, Provinsi Jawa Tengah
Subjects:	T Technology > T Technology (General) > T58.4 Managerial control systems Information technology. Information systems (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Iko Ganda Putra Sidauruk
Date Deposited:	10 Jan 2025 03:30
Last Modified:	10 Jan 2025 03:30
URI:	http://repository.unsri.ac.id/id/eprint/163611

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