Machine learning techniques have been widely adopted over the last few decades, especially in fisheries. This study aimed to determine the best practice of machine learning techniques with a decision tree algorithm in reducing the mortality rate of red tilapia (Oreochromis niloticus x Oreochromis mossambicus) fingerlings raised in outdoor earthen ponds with a recirculating aquaculture system. The study phase begins with collecting water quality parameters. The parameters were measured in the form of dissolved oxygen (mg L-1), pH, temperature (°C), total ammonia nitrogen (mg L-1), nitrite-nitrogen (mg L-1), alkalinity (mg L-1), transparency (cm), and mortality rate (fish day-1). Data Modelling was carried out using 10-fold cross-validation. The results of the performance measurement obtained an accuracy of 89.67% with ± 5.11% (micro average: 89.60%), a precision of 86.71% ± 18.02% (micro average: 80.00%), and recall of 72.50% ± 24.86% (micro average: 71.79%), with the most influential water quality parameter being nitrite-nitrogen (mg L-1). Based on the results of this study show that data classification using a decision tree algorithm can be used as a reference to determine the decisions or actions of fish farmers in reducing the mortality rate of red tilapia fingerlings raised in outdoor earthen ponds with a recirculating aquaculture system.
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