<![CDATA[This research aims to implement an automated nutrition and pH control system in NFT hydroponic system based on ANN control. NFT hydroponics involves growing plants without soil as a medium. In hydroponics, it is essential to continuously control the nutrient levels and pH of the solution. However, manual control performed by humans continuously is inefficient and time-consuming.The ANN method is used to model and predict the output actuators based on sensor input in the NFT hydroponic system. This ANN architecture consists of several layers with the following number of neurons: input layer 2, first hidden layer 128, second hidden layer 64, and output layer 3, representing multipleoutputs. The ANN training process involves classifying the data samples using various hyperparameters.The research findings demonstrate the ANN classification model successfully applied to control pH and nutrient levels through the predicted output actuators. The pump actuators are activated according to input received from the TDS and pH sensors. Through the variation of hyperparameters, the classification model with a test_size: 0.3, epoch: 400, batch_size: 32, and random_state: 42 provided the best performance in prediction. This ANN classification model achieved the best results in model testing with an accuracy rate: 97.96% from 49 data.]]>
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