Clustering Data Penduduk Desa Menggunakan Algoritma Mean Shift - Maulani, Tedi; Haerani, Elin; Wulandari, Fitri; Oktavia, Lola (TIN: Terapan Informatika Nusantara, 2025)
Social welfare remains a serious challenge in Indonesia, including in Riau Province, which, despite its abundant natural resources, still struggles with unequal distribution of welfare. One of the government’s efforts to address this issue is through social assistance programs. However, identifying the right beneficiaries remains problematic. This study aims to cluster residents of Desa Bina Baru using the Mean Shift algorithm to support more targeted social aid distribution. The clustering results were evaluated using the Silhouette Score to measure their quality. The optimal clustering was achieved at a quantile of 0.9, with the highest Silhouette Score of 0.5747, producing nine clusters with varying socioeconomic characteristics. Based on the analysis, clusters 2, 1, 5, and 6/7 were identified as the most eligible groups to receive government aid due to economic pressure, high number of dependents, and inadequate housing conditions. This prioritization is crucial for more accurate, data-driven distribution of aid and provides valuable insights to support sustainable poverty alleviation strategies in Desa Bina Baru.