Implementation Data Mining With K-Means Algorithm For Clustering Distribution Rabies Case Area In Palembang City

This research aims to classify the vulnerability of Rabies cases by using the CRISP-DM methodology through the process of business understanding, data understanding, data preparation, modeling, evaluation and deployment. The data that has the same characteristics are grouped in a single cluster then...

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Bibliographic Details
Published in:Journal of physics. Conference series 2020-04, Vol.1500 (1), p.12121
Main Authors: Rahayu, Kintan, Novianti, Leni, Kusnandar, Meivi
Format: Article
Language:English
Subjects:
Algorithms
Cluster
Clustering
CRISP-DM
Data mining
Expenditures
K-Means
Physics
Rabies
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Summary:This research aims to classify the vulnerability of Rabies cases by using the CRISP-DM methodology through the process of business understanding, data understanding, data preparation, modeling, evaluation and deployment. The data that has the same characteristics are grouped in a single cluster then the data with different characteristics are grouped in another cluster. The algorithm used in clustering technique is K-Means algorithm. In this case, the cluster is divided into three clusters. The first cluster is a result of an expenditure that has an area with very vulnerable cases of rabies, the second cluster is an expenditure result that has an area with the vulnerable level of rabies cases, the third cluster is the result of expenditure has areas with no vulnerable cases of rabies.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1500/1/012121