Abstract:
The Covid-19 pandemic entered Indonesia in March 2020, so the government imposed
restrictions on people's movement in various regencies. The imposition of restrictions
on people's movement will have an impact on the economy to the point of poverty.
Poverty is influenced by several factors such as population, health, education,
employment and economic factors. The poverty of a district/city in Indonesia is grouped
to assist the government in alleviating poverty more efficiently. The process of grouping
data in data mining is to group districts/cities in Indonesia based on factors that affect
poverty with the K-Medoids and CLARA algorithms, then compare the two methods
based on the average value of the ratio of the standard deviations. The variables used in
this study consisted of 4 variables, namely human development index (HDI), gross
regional domestic product (GRDP), unemployment rate, and population density. The
results of this study indicate that using the K-Medoids obtained 2 clusters, while using
the CLARA algorithm obtained 3 clusters. Based on the results of grouping the two
algorithms, the best algorithm was obtained using cluster validation, namely the
CLARA algorithm because it has the average value of the ratio of the smallest standard
deviation of 0.106.
