Analisis Cluster Single Linkage Berdasarkan Potensi Desa Di Kabupaten Kutai KartanegaraTahun 2019

  • Suyanto Suyanto Laboratorium Statistika Komputasi FMIPA Universitas Mulawarman
  • Syaripuddin Syaripuddin Laboratorium Matematika Komputasi, FMIPA Universitas Mulawarman
  • Wasono Wasono Laboratorium Matematika Komputasi, FMIPA Universitas Mulawarman

Abstract

Data mining is a step in the process of Knowledge Discovery in Database (KDD) which consists of the application of data analysis and the discovery of algorithms that produce certain enumerations of patterns in the data,Cluster Analysis is one of the methods in multivariate statistical analysis that is used to group objects into groups based on their characteristics, so the objects in one group have more homogeneous characteristics compared to objects in other groups. Single Linkage is a clustering process based on the closest distance between objects. If two objects are separated by a short distance, then the two objects will merge into one cluster. This study aims to obtain a cluster of village potential in Kutai Kartanegara Regency in 2019, based on the variable availability of educational facilities, the availability of health facilities, the availability of health workers, the availability of coin / card public telephones, the existence of lodging, the existence of market buildings, the existence of supermarkets, the existence of banks, the population obtaining credit facilities, the existence of other Non KUD cooperatives., Based on the results of the analysis, it can be seen that, Clusters formed in the grouping of potential villages / villages in Kutai Kartanegara Regency using a single linkage method are as many as 2 clusters.

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Published
2021-06-22
How to Cite
SUYANTO, Suyanto; SYARIPUDDIN, Syaripuddin; WASONO, Wasono. Analisis Cluster Single Linkage Berdasarkan Potensi Desa Di Kabupaten Kutai KartanegaraTahun 2019. EKSPONENSIAL, [S.l.], v. 12, n. 1, p. 59-64, june 2021. ISSN 2798-3455. Available at: <https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/761>. Date accessed: 27 apr. 2024. doi: https://doi.org/10.30872/eksponensial.v12i1.761.
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Articles