IMPLEMENTASI ALGORITMA Q-LEARNING UNTUK PENENTUAN RUTE PENANAMAN DAN PENYIRAMAN PADA PROTOTYPE SMART FARMING
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
RAJAGUKGUK, JOHANDRE ILHAM
Prayitno, Budi
Palupiningsih, Pritasari
Prayitno, Budi
Palupiningsih, Pritasari
Subject
Teknik Informatika
Datestamp
2023-05-31 03:58:51
Abstract :
The agricultural revolution 4.0 which consists of the internet of things, artificial intelligence, human machine interface and robotic technology to encourage the development of innovation in agriculture. At this time, the process of planting and watering plants in Indonesia is still carried out using traditional tools, so it takes a long time and also requires more manpower. The purpose of this research is to build a tool where the process of planting and watering the plants is done automatically without the need for human assistance. To build this tool, machine learning is needed to determine the planting and watering routes. One technique that can be used in determining the route of planting and watering is using the Q-Learning algorithm. The results of this study are a smart farming prototype equipped with soil moisture and DHT11 sensors and the ESP8266 WiFi module to send sensor value readings to websites and android applications as a monitoring system. The q-learning algorithm will carry out learning to obtain data from the environment so that it can determine the best route that can be chosen for planting and watering mustard plants. The smart farming prototype is able to implement the q-learning algorithm in it so that this tool is able to carry out the process of planting and watering plants automatically and is expected to help farmers, especially mustard greens farmers.
The agricultural revolution 4.0 which consists of the internet of things, artificial intelligence, human machine interface and robotic technology to encourage the development of innovation in agriculture. At this time, the process of planting and watering plants in Indonesia is still carried out using traditional tools, so it takes a long time and also requires more manpower. The purpose of this research is to build a tool where the process of planting and watering the plants is done automatically without the need for human assistance. To build this tool, machine learning is needed to determine the planting and watering routes. One technique that can be used in determining the route of planting and watering is using the Q-Learning algorithm. The results of this study are a smart farming prototype equipped with soil moisture and DHT11 sensors and the ESP8266 WiFi module to send sensor value readings to websites and android applications as a monitoring system. The q-learning algorithm will carry out learning to obtain data from the environment so that it can determine the best route that can be chosen for planting and watering mustard plants. The smart farming prototype is able to implement the q-learning algorithm in it so that this tool is able to carry out the process of planting and watering plants automatically and is expected to help farmers, especially mustard greens farmers.
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