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

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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.

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Inventory Code Barcode Call Number Location Status
2507000009T163611T1636112024Central Library (Reference)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1636112024
Publisher
Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer.,
Collation
xvii, 113 hlm.; ilus.; tab.; 29 cm.
Language
Indonesia
ISBN/ISSN
-
Classification
518.107
Content Type
Text
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Algoritma
Prodi Teknik Informatika, Fakultas Ilmu Komputer
Specific Detail Info
-
Statement of Responsibility
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No other version available

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