Penerapan Data Mining Untuk Clustering Data Penduduk Miskin Menggunakan Metode K-Means - Parjito Parjito; Permata Permata (Ainet : Jurnal Informatika, 2021)
Poverty that occurs in the community is a major problem that must be an important concern for the government in any country in various provincial, district and city governments, such as providing assistance to the poor in the form of allowances that can relieve the community and help the community, Community Health Insurance, Rice for the Poor (Raskin), Education Cost Assistance for Academic Achievement Improvement (BBP-PPA) Tuition Fee Assistance which is only intended for prospective students who are unable or poor (BIDIKMISI), and other programs that can help the poor, but in reality the problem of poverty cannot be overcome. Suka Bhakti Village carried out grouping of poor and non-poor communities based on basic determinant variables, namely the latest education of members or family heads, ownership of several family assets, total family income from both the head of the family and a combination of family members, the area of the building owned, the type of floor used, and the type of wall. The use of data mining with cluster techniques through the K-Means method can group community data to get clusters that should be able to receive social assistance from the government. The results of the validity calculations carried out, the results obtained for the number of cluster 1 are 130 data and for cluster 2 are 682. Testing this data is carried out using the Davies-Bouldin Index (DBI) method. The resulting number of suitable clusters is 2 clusters with a value of 0, 1643 which means that the cluster is suitable for grouping community data.