@thesis{thesis,
		author={Shoofiyah  .},
		title ={Deteksi Kanker Kolorektal Berbasis Jaringan Syaraf Tiruan},
		year={2020},
		url={https://repository.ittelkom-pwt.ac.id/6055/},
		abstract={Based on the image colon, there is a problem in the colon, which is detecting the presence or absence of harmful polyps in the colon. Based on these problems, we need tools to detect colorectal cancer by building a colorectal cancer detection system using digital image processing technology and use backpropagation learning methods. In this research for the detection of colorectal cancer a process of normalizing data is needed in an image, the goal is to produce the desired output. The results of the colorectal cancer detection system testing using backpropagation neural networks produce an accuracy level of 90% which is tested on 15 normal images and 15 image polyps as test data samples. Then, the results of the colorectal cancer detection system 
training using backpropagation neural networks produce an accuracy rate of 100% which is tested on 15 normal images and 15 image polyps as training data samples. Also, getting an accuracy of 95% for 60 image data in all conditions, so that colorectal cancer is not detected to detect subjectively. The application of colorectal cancer detection system using the backpropagation learning method can help users detect colorectal cancer with a lot of image data at once and takes a short time.
Keywords: Artificial Neural Network, Backpropagation, Colorectal Cancer Detection}
		}