Comparison of Poverty Clustering Results based on Distance Measurement with the Complete Linkage Method in Indonesia

Abstract

Every year, population growth in Indonesia increases and has the potential to trigger poverty.
Poverty indicators include the number of poor people, per capita expenditure, human
development index, average years of schooling, and unemployment. The clustering of regions
is necessary for the government to be more effective in development. One of the methods
used is cluster analysis, a statistical technique that groups objects based on similar
characteristics. This research compares the results of clustering poverty in Indonesia's
Regency/City in 2023 using the complete linkage method, which is based on the farthest
distance. The distances analyzed include Euclidean, Square Euclidean, Manhattan, and
Minkowski, resulting in two clusters at each distance. Minkowski proved to be the best
distance with the smallest standard deviation ratio, which was 1.518 for cluster 1 and 2.225
for cluster 2, compared to the other distances. These results show that the Minkowski method
is superior in clustering poverty areas in Indonesia.