Action Recommendation Model Development For Hydromon Application Using Deep Neural Network (DNN) Method

Mugi Praseptiawan, Muhammad Nadhif Athalla, Meida Cahyo Untoro


Controlling hydroponic plants, which is currently being carried out manually, can be said to be less effective because it still involves the hard work of farmers to continuously monitor the condition of the hydroponic plants. Therefore, the general objective of this research is to develop a model that can be used as a recommendation system for actions that farmers need to take based on hydroponic crop conditions. The model formed with this machine learning method will then be used in the Hydromon application which allows farmers to manage and monitor the condition of hydroponic plants and take action based on the recommendations given. This model was developed using a deep neural network algorithm consisting of five layers with the help of the TensorFlow framework. The results show that the model is accurate with an accuracy value of 96.47% on the test data to classify plant conditions so that it can be used in the Hydromon application.

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