PENERAPAN METODE K – HARMONIC MEANS DALAM PENGELOMPOKAN KABUPATEN/KOTA (Studi Kasus: Kemiskinan di Pulau Kalimantan Tahun 2020) - Dwi Indra Yunistya; Rito Goejantoro; Fidia Deny Tisna Amijaya (Department of Mathematics, Hasanuddin University, 2022)
Poverty is one of the problems that faced by every country in the world, especially in developing countries, one of them is Indonesia. Poverty alleviation that is currently planned is no longer uniform, but it is necessary to pay attention to the condition of each dimension causing poverty in an area, so it is necessary to group districts/cities on the Kalimantan Island based on poverty. Cluster analysis is classifying the data (objects) only based on the information discovered in the data that describes the objects and the relations between them. The method used in this research is K-Harmonic Means method. K-Harmonic Means is a non-hierarchical clustering algorithm that uses the average harmonic distance from each data point to the cluster center. This study aims to classify the District/City in Kalimantan Island based on poverty indicators and obtain the silhouette coefficient value from the optimal cluster analysis. Based on the results of the analysis of the K-Harmonic Means method, the optimal number of clusters is 2 clusters with parameter (p) of 4. Cluster 1 consists of 11 Districts/Cities and Cluster 2 consists of 45 Districts/Cities. Silhouette coefficient value for data validation of District/City clustering results on Kalimantan Island using the K-Harmonic Means method, namely 2 clusters with parameter (p) of 4 is 0.323 which states that the resulting cluster structure in this grouping is a weak structure.