<![CDATA[This study aims to cluster pond water quality data to support decision making in fish farming management. The Gaussian Mixture Model (GMM) method is used as a probabilistic approach in clustering water quality parameters, namely pH, temperature, turbidity, and total dissolved solids (TDS). Data were collected from ponds in Kuala Kerto Village, North Aceh Regency, which is a traditional fish farming area. Before clustering, the data were cleaned from outliers and normalized using the Z-score method to improve the modeling quality. The model evaluation results showed that the GMM with 3 clusters provided the best results with a Silhouette Score of 0.55, Davies-Bouldin Index of 1.01, and the lowest BIC Score. Based on the standards for aquaculture water quality (pH 6.5–8.5, TDS <3000 mg/L, turbidity <300 NTU), each cluster was interpreted into good, moderate, and poor quality categories. Visualization of the results using PCA shows quite clear separation between clusters. This research provides a practical contribution in helping fish farmers monitor and evaluate pond water conditions in a more structured and data-driven manner. Keywords: Gaussian Mixture Model, Pond Water Quality, Clustering, Z-Score, Silhouette Score ]]>
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