Analisis Algoritma Fuzzy C-Means Untuk Pengelompokan Data Keluarga - Wahyu Cahyadi; Elin Haerani; Alwis Nazir; Iwan Iskandar (TIN: Terapan Informatika Nusantara, 2025)
Mapping the socio-economic conditions of the community plays a crucial role in supporting targeted development planning at the village level. This study aims to apply the Fuzzy C-Means (FCM) algorithm to cluster families in Bina Baru Village based on social, economic, and household environmental indicators. The variables used include family size, income sources, physical condition of the house, basic facilities, as well as monthly expenditure and income levels. This study uses population data from Bina Baru Village, consisting of 1,000 entries with 16 variables. The FCM algorithm was chosen for its ability to accommodate multiple degrees of membership (fuzzy membership), making it more adaptable in capturing the diversity and ambiguity of socio-economic characteristics. The results show that FCM produces two main clusters: Cluster 0, with 440 members, reflects families with middle to lower economic conditions, permanent housing, and adequate basic facilities; and Cluster 1, with 560 members, represents families with lower economic conditions, semi-permanent housing, and relatively smaller family sizes. Evaluation using the Xie–Beni index (35.4976), Fuzzy Partition Entropy (0.6843), and Fuzzy Cluster Index (0.4468) indicates that the two-cluster model has the best clustering quality compared to other numbers of clusters. Overall, the Fuzzy C-Means algorithm is effective in mapping variations in family welfare and can be used as a basis for formulating development policies and data-driven community empowerment programs in Bina Baru Village.