@thesis{thesis,
		author={Affandi  Riki Ruli and Manjawakang  Abdul Haris and Meidilaga  Gema Naufal},
		title ={Identifikasi Pertumbuhan Abnormal Pada Tanaman Padi Menggunakan Metode Faster Region Convolutional Neural Network},
		year={2022},
		url={http://156.67.221.169/5800/},
		abstract={In the growth of rice plants there are abnormalities in growth and delays in 
anticipating which will lead to sub-optimal yields. This is caused by blas disease and blight. 
To assist in the fast handling, this study designed a website-based expert system with the 
Faster Region Convolutional Neural Network method approach which aims to help solve 
problems because this method functions to process image data and the image data used is 
leaf image data on rice plants. This study resulted in an accuracy of 99.32% in identifying 
and classifying diseases using leaf imagery. Evaluation using confusion matrix results in 
accuracy of 94%, precision of 100%, recall of 74%, and f1-score of 85%.}
		}