HYDROPONIC NUTRIENT CONTROL STUDY ON THE GROWTH OF WATER SPINNACH (IPOMOEA AQUATIC F.) BASED ON THE INTERNET OF THINGS (IOT).

• Main Author: Azriel De Borgot J, Joehannes
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/72609
• Institution: Institut Teknologi Bandung

The hydroponic method helps increase vegetable production by using water which is very effective but this method still has drawbacks to changes in the environment or physical parameters so that production is not optimal. To increase production, a system for controlling nutrient levels was created. The system is made a growth model based on machine learning and data obtained from the Internet of Things (IoT) system. There are 4 environmental sensors that retrieve environmental data in the form of ambient temperature, ambient humidity, light intensity, water temperature and Total Dissolved Solids (TDS) content. After the system is created, nutrient injection experiments will be carried out with variations in the ratio of nutrients A and B if the Hydroponic PPM value is less than 1000 with a nutrient content of 9 ml : 9 ml : 1 liter and 4.5 ml : 4.5 ml : 1 liter. Then all the data is sent to the gateway in the form of the Long Range (LoRa) protocol and converted into the Message Queuing Transport Telemetry (MQTT) protocol. The data is processed to produce a graph of physical parameters against time and a graph of growth over time, both of which will be used as datasets. The dataset will be input into the Random Forest and Gradient Boosting Training to obtain a growth model for a hydroponic nutrient control system. The growth model of the Random Forest hydroponic nutrition control system has an absolute error value of 17.5% and R2 0.91688. As well as good growth for Bok Choy plants is the injection of nutrients A and B and water of 4.5ml:4.5ml:1 L