Comparative Analysis Of K Means And K Medoids Algorithms In Determining Social Assistance In Padang Sidimpuan City, North Sumatra - Fadel Muhammad Siregar; Ujang Juhardi; Muntahanah Muntahanah; Agung Kharisma Hidayah (Jurnal Komputer, Informasi dan Teknologi, 2024)
The government's efforts to tackle poverty are by issuing several programs that can help meet people's needs. However, in practice, it is not uncommon for the distribution of aid or other government programs to not be on target, because there are no clear procedures and calculations in determining which people are entitled to receive aid. Therefore, it is necessary to have a calculation mechanism that involves data on the demographic characteristics of the community using clustering. In this clustering process, there are 2 algorithms that are often used, namely K-medoid and K-means. The research aims to classify which communities are a priority for receiving assistance and which are not a priority. In order to get more accurate results, this research also tested the two clustering algorithms to get the best algorithm and the best number of clusters based on the dataset owned by looking at the Davies Bouldin Index (DBI). This research concluded that with the data set the best algorithm was K-medoids and the number of clusters was 2 with a DBI value of 1.332. Then, in the results of the clustering carried out from a total of 1031 data analyzed, it was found that 396 residents were eligible or made priority for receiving assistance and 635 residents who were not yet included in the priority list for receiving assistance.