@thesis{thesis,
		author={Agtriadi  Herman Bedi and Ningrum  Rahma Farah and RAMADANI  ANDI PUTRI MAULANA},
		title ={PENGENALAN TULISAN STENOGRAFI MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK BERBASIS WEB},
		year={2022},
		url={http://156.67.221.169/4911/},
		abstract={The workers, especially those who work as journalists and secretaries, have problems in
recognizing shorthand writing. The need for media that can convert stenographic writing
into a language that is easy to understand. Therefore, this study proposes to do stenographic
image processing which then classifies the image as a supporting medium for journalists,
secretaries, and other professionals in recognizing shorthand writing. The image is
processed using the Convolutional Neural Network method, then the results are displayed
in a website-based interface. The limit for the processed word category is 20 nouns. A total
of 100 handwritings for each word class were collected so that the total images obtained
were 2000 stenographic handwritten images. The results of image processing state that the
average accuracy produced is 81%. From these results, it was concluded that the model
formed using the Convolutional Neural Network method in shorthand writing was
successfully recognized with a good level of accuracy.}
		}