<![CDATA[The increasing volume of domestic wastewater, particularly greywater, has raised the demand for intelligent and adaptive treatment systems to support efficient water reuse. This study aims to develop a classification model for filtration media types (physical, chemical, and biological) based on water quality data using the Random Forest algorithm. Initial labeling was conducted using the K-Means Clustering method on a publicly available dataset simulated as greywater, based on ten key water quality parameters relevant to irrigation and environmental standards. Model evaluation demonstrated excellent classification performance, with a macro F1-score reaching 0.97 and consistent results in both 5-fold and 10-fold cross-validation. These findings indicate that the proposed model can be integrated into an IoT-based biofiltration system as an automated classification logic to support adaptive, efficient, and reusable household wastewater treatment in the context of irrigation.]]>
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