Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika
Marshelle, Sean
Unknown Affiliation
Decision Sciences, Operations Research & Management Education Mathematics

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
PERBANDINGAN K-MEDOIDS DAN CLARA (Clustering Large Application) PADA DATA POPULASI TERNAK DI INDONESIA Ardhani, Rizky; Marshelle, Sean; Fitrianto, Anwar; Erfiani, Erfiani; Jumansyah, L. M. Risman Dwi
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.764

Abstract

This study compares the K-Medoids and CLARA (Clustering Large Application) methods for livestock population data in Indonesian districts and cities. Calculating the distance between points and objects in the data, K-Medoids is a method for clustering based on data points (medoids). A larger dataset is divided into several samples for comparison in CLARA, an extension of the K-Medoids approach. The CLARA method analysis results show that three clusters are the ideal number. The ideal number of clusters in a K-Medoids cluster analysis is two. The Silhouette Score (SS), Davis-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI) are the metrics that are measured. The evaluation of the comparison results shows that the CLARA method has an SS value of 0.66, while K-Medoids has an SS value of 0.62. The comparison of the CLARA and K-Medoids approaches yielded DBI values of 1.38 and 1.92, respectively, and 197.54 and 132.73 for CHI. The findings indicate that, in comparison to the K-Medoids approach, the SS value for the CLARA method is closer to 1, and that the CHI value derived from the CLARA method is likewise greater. The K-Medoids approach has a higher DBI value than the CLARA method, where a lower DBI value denotes superior performance. The CLARA approach is the most effective way to do cluster analysis on livestock population data in Indonesian districts and cities, according to the findings.
Co-Authors Ardhani, Rizky Erfiani Erfiani Fitrianto, Anwar Jumansyah, L. M. Risman Dwi