Comparison of K-Means and Fuzzy C-Means for Optimizing Tuberculosis Management and Healthcare Service Allocation in Bojonegoro - Muiz, Riko Al (Universitas Nahdlatul Ulama Sunan Giri, 2024)
Background: According to the 2022 publication by BPS (Statistics Bureau) of Bojonegoro Regency, there were 1,765 tuberculosis cases spread across all districts in Bojonegoro. This number is disproportionate to the availability of healthcare workers, which totaled only 1,261, comprising medical personnel, nurses, midwives, and pharmacists. Objective: This study aims to cluster districts in Bojonegoro Regency based on tuberculosis cases and healthcare workforce data by comparing the K-Means and Fuzzy C-Means methods. The objective is to identify which districts require more attention and which are already in better condition. Methods: The best clustering method was determined using the Sum of Squared Error (SSE) criterion. The data used in this study was sourced from the Statistics Bureau, containing information on tuberculosis cases and the number of healthcare workers in each district.. Results: The result shows that K-Means achieved a lower SSE (4704.031) compared to Fuzzy C-Means (4854.247), which divided the district into 4 clusters: low, medium, and high. By categorizing the districts into these clusters, the Bojonegoro government is expected to better target its interventions and resources. Moreover, the government can evaluate districts with high tuberculosis cases to implement specific strategies. Conclusion: This study concludes that K-Means with 4 clusters is the most effective method for this type of clustering.