@thesis{thesis,
		author={Haris  Abdul and Safiera  Nurin Masyitha and Siregar  Riki Ruli A.},
		title ={METODE CONVOLUTIONAL NEURAL NETWORK SEBAGAI IDENTIFIKASI KEBUTUHAN NUTRISI TANAMAN PADI HIDROPONIK},
		year={2022},
		url={http://156.67.221.169/5783/},
		abstract={Rice is one of the most important staple crops in the world. Most regions in the world 
make rice as a staple food, especially in Indonesia. Nutrition is one of the important things in 
plant growth and development. Lack of nutrients in plants can affect the growth process and the 
quality of plants that are ready to be harvested. The Convolutional Neural Network method was 
chosen to identify the nutritional needs of hydroponic rice plants. In this study, the dataset used 
was 1190. Rice leaf images which were divided into 2 classes, Enough and Less, which were 
tested with a comparison of 80% as data train, 10% as data test, and 10% as validation data. 
There are three architecture models used in this research, VGG16, MobileNet, and Xception using 
Jupyter and Google Colaboratory as tools. The training process was carried out by entering 10 
epochs and batch sizes of 32 and 64. The best accuracy results obtained were VGG16 78,15% 
and 86,47%, MobileNet of 82,69% and 86,55%, Xception of 82,33% and 88,24%. Meanwhile, the 
best overall accuracy results were obtained from the Xception model of 88,24% with batch size 
onput of 32, and the tools used were Jupyter.}
		}