Implementasi Neural Network dalam Mengendalikan Input dan Output pada Penyiraman dan Pemupukan Tanaman Otomatis Berbasis IoT
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Abstract
Agriculture is an important sector in human life. However, in practice, agriculture still faces many challenges such as difficulties in optimally controlling watering and fertilizing crops. To overcome this problem, an automatic plant watering and fertilizing system was developed as an alternative solution. This system can help farmers control watering and fertilizing plants automatically and optimally based on soil and plant conditions measured by sensors. In practice, automation systems for watering and fertilizing plants usually still use simple rules based on the experience or theory of farmers. Therefore, the implementation of a neural network in an automated system of watering and fertilizing plants can help predict irrigation needs for plants accurately and control watering and fertilizing automatically. To prove the effectiveness of the proposed method, testing was carried out using the Neuroph Studio application. From the test results, the total error results for the tool in controlling the output are less than 0.01 of the desired output value. These results are good and indicate that the neural network is an effective method of choice as a learning parameter. In addition, by using IoT technology, the automation system can be connected to the internet, so that it can be accessed remotely and monitored in real-time. This makes it easier for users to control the automation system and monitor the state of the plants.
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