Comparison Performance of K-Medoids and K-Means Algorithms In Clustering Community Education Levels

Diana Dwi Aulia; Nurahman Nurahman

doi:10.23887/janapati.v12i2.59789

Authors

Diana Dwi Aulia Universitas Darwan Ali
Nurahman Nurahman Universitas Darwan Ali

DOI:

https://doi.org/10.23887/janapati.v12i2.59789

Keywords:

Education, Data Mining, Clustering, K-Means, K-Medoids

Abstract

Education is a mandatory right of all citizens and the key to the nation's superiority in global competition that must get top priority to be examined critically and comprehensively. It is known that compulsory education is at least 12 years, but not all people can do it because of minimal economic conditions. In past years, COVID-19 has also had an impact on the economy, school dropout rates, and falling academic achievement, for example in Central Kalimantan. The size of Central Kalimantan, however, makes it difficult for the government to identify the areas with the worst levels of education. To determine which regions fall into the low and high education categories, it is required to group the province's educational levels. This study also compares two algorithms by measuring their accuracy. By looking at which algorithm has the lowest Davies Bouldin Index (DBI) value, the best degree of performance can be ascertained. To process the data from as many as 1,565 sources, data mining techniques, including the clustering method, were used. K-Means and K-Medoids algorithms were employed in this work as clustering techniques. Based on the outcomes of the cluster created, both algorithms are also put to the test for performance. The results of this study obtained 6 clusters in K-Means with the lowest DBI value of -0.439, while the results in K-Medoids were in 3 clusters with the lowest DBI of -0.866. Based on accuracy testing using DBI, it is known that K-Means results are more optimal with the lowest DBI value in the grouping of education levels compared to K-Medoids. It is also known from the formation of 6 clusters of the K-Means algorithm that the low education level is in cluster_0 which is 1484 villages and the higher education level is as many as 3 villages in cluster_3.

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