Design of Classification of Shrimp Pond Water Quality Based on Random Forest Algorithm

Monitoring the water quality of shrimp ponds using conventional methods involves periodic water sampling manually performed by pond farmers from various locations within specified intervals. This method is not only time-consuming but also susceptible to uncertainties, as rapid changes in water quality may not be effectively detected. Hence, a machine learning system with a Random Forest algorithm is proposed. The system can monitor water quality in real-time, continuously, and automatically. Moreover, it can process realtime data and classify the water quality of shrimp ponds based on predefined variables against the standard values established in the Indonesian National Standards (SNI). The water quality classification process involves comparing pH, temperature, and TDS features with the SNI. The research results indicate that the Random Forest classification model achieved accuracy and precision of 94.91 percent and 97.44 percent, respectively. These performances are better than those of KNN and SVM. Moreover, the most influential feature of the output model is TDS, with a value of 50.7 percent.