KOMPARASI PERFORMA K-MEANS DAN FUZZY C-MEANS

Abstract

This study aims to test the performance of the K-Means Cluster method with Fuzzy C-Means. The data used is data from the Inclusive Economic Development Index in 34 provinces in Indonesia in 2021. The data is sourced from Bappenas. The optimum number of clusters suggested using the Elbow method technique is as many as 4 clusters. By paying attention to the silouhette value the K-Means method is as good as the Fuzzi C-Means. However, the K-Means method is better than the Fuzzy C-Means model when viewed based on the criteria of smaller AIC and BIC values and a larger R 2. The provinces of Papua and West Papua have negative cluster means values for all variables so it is said that it is still lacking for all pillars of the IEDI. On the other hand, the provinces of DI Yogyakarta and DKI Jakarta have positive cluster means values for all variables so that they are said to be good in terms of the economy and opportunities and access but still have high inequality and poverty. Comprehensive and targeted policies are needed so that inclusive economic development in Indonesia can be evenly distributed and increased every year