The Implementation of the Fuzzy C-Means Method in Handling Outlier Data in the 2021 Village Potential Data of Bengkulu Province - Panjaitan, Intan Juliana; Indahwati, Indahwati; Afendi, Farit Mochamad (ComTech: Computer, Mathematics and Engineering Applications, 2025)
Clustering groups aims to ensure similarity within clusters and disparity between them. The research evaluated the Fuzzy C-Means method’s effectiveness in clustering large datasets containing outliers, focusing on the 2021 Village Potential data from Bengkulu Province. The dataset, comprising 1,514 observations from villages and urban villages, provided a comprehensive resource for understanding regional development. Outliers, a common challenge in cluster analysis, were detected using univariate and multivariate methods, revealing substantial variability. PCA was applied, improving clustering quality to address multicollinearity among variables. In the results, the fuzzifier (w) parameter in the FCM method plays a crucial role in controlling the degree of membership for data points in clusters, which can potentially reduce the impact of outliers, enhancing clustering robustness and accuracy. The FCM method effectively produces clusters with high intra-cluster homogeneity and inter-cluster heterogeneity. Using the Elbow method, three optimal clusters are identified. Cluster 1, dominated by villages in Bengkulu City, is the most advanced, with superior infrastructure and services, but the fewest villages business units, necessitating economic empowerment. Cluster 2, comprising villages in North Bengkulu Regency, demonstrates moderate development but suffers from poor transportation access, requiring improvements to support socio-economic activities. Cluster 3, dominated by villages in Kaur Regency, is the least developed, with limited basic services and infrastructure, highlighting the need for substantial investments in governance and essential services. These findings provide actionable insights for village development in Bengkulu Province, supporting targeted policies tailored to each cluster’s unique characteristics.