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

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, humandevelopment index, average years of schooling, and unemployment. The clustering of regionsis necessary for the government to be more effective in development. One of the methodsused is cluster analysis, a statistical technique that groups objects based on similarcharacteristics. This research compares the results of clustering poverty in Indonesia'sRegency/City in 2023 using the complete linkage method, which is based on the farthestdistance. The distances analyzed include Euclidean, Square Euclidean, Manhattan, andMinkowski, resulting in two clusters at each distance. Minkowski proved to be the bestdistance with the smallest standard deviation ratio, which was 1.518 for cluster 1 and 2.225for cluster 2, compared to the other distances. These results show that the Minkowski methodis superior in clustering poverty areas in Indonesia.