@thesis{thesis,
		author={Haris  Abdul and Samberi  Berlince and Sangaji  Iriansyah},
		title ={PENERAPAN ALGORITMA CONVOLUTIONAL NEURAL NETWORKS (CNN) UNTUK IDENTIFIKASI PENYAKIT PADA TANAMAN CABAI},
		year={2022},
		url={http://156.67.221.169/5776/},
		abstract={Diseases of a plant will greatly affect the yield of such a crop. If the disease is not treated 
immediately, it can damage the crop and result in crop failure which will affect the economy. 
Therefore, the identification of plant diseases, especially in chili peppers, is very important in the 
process of plant care. Chili plants are a type of plant that is widely grown in Indonesia, chili plants 
are also plants that are much loved by almost all people and are one of the food ingredients needed 
with a lot of demand, it will be necessary to increase chili land. The larger or larger the land area, 
the greater the effort required to care for and supervise the plants. With the development of 
technology today, there will be opportunities in identifying plants automatically using a computer 
system. Using image processing the disease is captured through the camera will be able to be 
analyzed and identified with a computer. So that in supervising plants will become easier and 
more efficient. There are several types of diseases in chili plants that are often found, namely 
yellow virus, stem rot, and leaf spot. A feature of the disease can be seen from the shape of the 
color of the leaves. In this study, disease identification was carried out using the CNN algorithm. 
CNN is used so that models can extract features and perform image classification automatically. 
Image data taken directly from the IP2TL Balitsa plantation of the Ministry of Serpong Jakarta. 
And the data was taken around 12.30 ? 14.00 noon, so that the image has a clear color. This model 
will be able to classify 3 types of leaf conditions including 3 diseased and convolutionary 
processes using Google Colab. This CNN model can produce 45% accuracy}
		}