Clustering Villages Based on Distance and Accessibility to Health Facilities Using the K-Means Method
Abstract
There are 47 very underdeveloped and 63 underdeveloped villages in Melawi regency. More than 50% of the villages have no health facilities, and the percentage of road lengths with good condition is only 20.53% in Melawi County. One of the most important factors influencing health problems is the physical aspect such as the availability of health facilities. In addition, the distance and easy access to health facilities also influence how quickly people are treated and vaccinated during the Covid 19 pandemic. The objective of this study is to determine the degree of accessibility of health facilities in villages by forming village clusters that are likely to be important to the government in ensuring treatment and distribution of Covid 19 vaccine. The clustering method used is the K-Means method with Euclidean spacing to calculate the spacing of the data and the Elbow method to determine the optimal number of clusters on the data, and the Silhouette coefficient evaluation method to test the degree of accuracy of the model created with K-Means. The results of the Elbow method showed the optimal number of clusters to be 2 clusters. Based on the results of the K-Means algorithm process, the clusters that have a larger average distance and access is rated as difficult are cluster 1 with 92 villages in it, and cluster 1 has a smaller average distance and access is relatively easy with 77 villages in it. The result of the evaluation with the silhouette coefficient is 0.299.
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