Selamat Datang Di Repository Universitas Royal

Effective management of social assistance is one of the important factors in
improving community welfare. However, the selection process for aid recipients at
the Air Hitam Village Office is still done manually, making it prone to targeting
inaccuracies. This study aims to develop a system for grouping social assistance
recipients based on welfare levels using the K-Means algorithm. This algorithm is
applied to classify individuals into three welfare categories: high, medium, and
low, based on variables such as income, number of dependents, income sources,
and housing conditions. The methods used in this study include data collection
from the village, processing using data mining techniques, and implementation in
a system based on Visual Basic and MySQL. The results of the study show that the
use of K-Means can improve efficiency and accuracy in classifying aid recipients,
thereby assisting the village government in distributing aid more precisely.
The final clustering results show that the community is divided into three clusters:
Cluster 1 (low welfare) with 15 individuals, Cluster 2 (medium welfare) with 22
individuals, and Cluster 3 (high welfare) with 7 individuals. With this system, it is
expected that a more transparent and effective social policy can be developed to
improve community welfare.
Keywords: K-Means; Social Assistance; Clustering; Welfare; Data Mining