PRINCIPAL COMPONENT K-MEANS SOFT CONSTRAINT BASED ON WELL-BEING INDICATORS IN ACEH PROVINCE

Winny Dian Safitri

doi:10.22373/cj.v7i2.17548

PRINCIPAL COMPONENT K-MEANS SOFT CONSTRAINT BASED ON WELL-BEING INDICATORS IN ACEH PROVINCE

Winny Dian Safitri

Abstract

The success of government policies can be from the state of the well- being indicators. This research was conducted to obtain district/city groupings based on the similarity of characteristics of the well-being indicators of each district/city in Aceh Province in 2022. The data used in the Aceh well-being indicator data for 2022 consists of 29 variables. The analysis method used is the principal component k-means soft constrain method. The background information data can be used as a provision to streamline the clustering algorithm by creating soft constraints which is found as the most appropriate algorithm. The results of this study indicate there are four district/city clusters in Aceh Province. The characteristics of the first cluster are that kindergarten and elementary school facilities are adequate, while the school enrollment rate needs to be improved. The characteristics of the second cluster are superior to the Gross Enrollment Rate (GER) and the population of university graduates, but still very lacking in school facilities. The third cluster is the cluster that is the center of well-being in Aceh, so this cluster is the cluster with the best well-being level. The characteristic of the fourth cluster is that it is very good in the school participation rate indicator, but it must increase early childhood school participation.

Keywords

Well-being, Clustering, Principal Component, K-means, Constrain

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DOI: http://dx.doi.org/10.22373/cj.v7i2.17548