Implementation of K-Means for Mapping Cleanliness Levels in Pancur Batu District
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Abstract
This study aims to classify areas in Pancur Batu District based on environmental cleanliness levels using the K-Means Clustering method, analyze the characteristics of each cluster based on cleanliness parameters, and develop a web-based mapping system to visualize the clustering results spatially. The study utilized 578 image data analyzed using five parameters, namely visible waste density, drainage condition, percentage of clean roads, percentage of households with trash bins, and waste collection frequency. The results showed that the areas in Pancur Batu District were successfully grouped into three clusters: Cluster 0 (Clean) consisting of 6 villages, Cluster 1 (Dirty) consisting of 12 villages, and Cluster 2 (Very Clean) consisting of 7 villages. The clustering results were then visualized through a web-based mapping system to facilitate users in identifying the distribution of cleanliness levels across the study area. Expert validation conducted by a geography specialist obtained a total score of 42 with an average score of 4.2, which falls into the good category. Therefore, the implementation of the K-Means Clustering method was able to effectively classify areas based on environmental cleanliness levels and provide useful information to support environmental cleanliness management and decision-making processes in Pancur Batu District.
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