Penerapan Data Mining Clustering terhadap Perekonomian di Kelurahan Sawah Lebar Menggunakan Algoritma K-Means - Ayi Purnama, Soni; Novrian, Willi; Ramadani, Niska (Masoem University, 2024)
This study aims to analyze the economic conditions of the community in Sawah Lebar Village, Ratu Agung District, Bengkulu City, Bengkulu Province, using the data mining clustering method based on the K-Means algorithm. The K-Means algorithm is applied to group economic data of the community based on several variables, such as Employment Status, Home Ownership, Dependents, Monthly Income, Monthly Expenditure and Age of Head of Family. Through this clustering process, several groups (clusters) are produced that identify different economic patterns in the region, namely Cluster_0 (C0), Cluster_1 (C1) and Cluster_2 (C2). In order for the results of this study to be accurate, the data processing in this study uses the Rapidminer Studio Application. From the results of the data processing that has been carried out, it was found that for C0 there are 17 data, C1 there are 16 Data and C2 there are 17 Data. Then, for the recommendation of the community that is recommended to receive government assistance, it is the community that is in the lowest economic class, namely Cluster_1 (C1), Cluster_2 (C2) is the community with the highest economy, while the community in Cluster_0 (C0) is the middle economy.