Data Mining using K-means method for feasibility selection of Non-cash food Assistance recipients in the Era of Covid-19 - Rusdiansyah, Rusdiansyah; Supendar, Hendra; Tuslaela, Tuslaela (Sinkron : jurnal dan penelitian teknik informatika, 2021)
All countries in the world are currently experiencing a severe economic crisis following the outbreak of the COVID-19 outbreak. In Indonesia, the Large-Scale Social Restriction (PSBB) policy is reported to have increased the number of poor people. Social assistance is a government program to improve the social welfare of the lower economic community. In carrying out the program, the central government and local governments coordinate with each other so that the program is right on target without any element of fraud. In the neighbourhood of Rukun Warga 001, Kelapa Dua Village, there are still obstacles in selecting the eligibility for social assistance recipients, namely Non-Cash Food Aid. The data on the poor are not in accordance with the actual conditions. In this study, to implementing data mining with the K-Means Algorithm. The K-Means Clustering algorithm is used to classify people who are classified as eligible to receive social assistance and those who are not entitled to receive social assistance. The data sample used is the data of Rukun Warga 001, Kelapa Dua Village. The results of this study indicate that cluster 1 with the appropriate category of receiving social assistance according to government programs in the Rukun Warga 001 neighbourhood of Kelapa Dua sub-district amounted to 13 families. And cluster 2 in the category of not eligible to receive social assistance amounted to 97 heads of families out of a total of 110 heads of families in RW 001.