Performance Analysis of the K-Means Algorithm in Classifying Organic and Inorganic Waste Types
Keywords:
K-Means Clustering; Organis Waste; Inorganis Waste; Classification; Data MiningAbstract
Waste management is one of the most pressing environmental challenges in Indonesia, particularly in effectively and efficiently distinguishing between organic and inorganic waste. The inability to properly classify waste types leads to suboptimal recycling processes and increased environmental burden. This study aims to implement and analyze the performance of the K-Means Clustering algorithm in classifying organic and inorganic waste based on physical attributes and waste composition data. The method employed involves preprocessing waste attribute data, applying the K-Means algorithm with optimal cluster number determination using the Silhouette Score. The results indicate that the K-Means algorithm successfully produced well-separated and cohesive clusters, demonstrating adequate classification performance in distinguishing organic from inorganic waste characteristics. These findings suggest that K-Means Clustering can serve as a reliable, computationally efficient, and interpretable foundation for developing automated waste classification systems. The practical implication of this study is to support data-driven decision-making in sustainable waste management programs, particularly at the community and local government scale in Indonesia.
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