@thesis{thesis,
		author={Andini  Yuli and Kuswardani  Dwina and Siregar  Riki Ruli A},
		title ={KLASIFIKASI CITRA CT SCAN HATI UNTUK DETEKSI PENYAKIT TUMOR HATI MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)},
		year={2022},
		url={http://156.67.221.169/5705/},
		abstract={Liver tumors can be caused by excessive cell growth in the liver organs. This study aims to 
determine the classification of Liver CT Scan images in the detection of Liver Tumor 
disease using the Convolutional Neural Network (CNN) method with the Inception 
ResnetV2 architecture which is based on helping ordinary people or medical personnel 
make a decision in diagnosing liver tumor disease early. The Convolutional Neural Network 
(CNN) method with the Inception ResnetV2 architecture was chosen because this method 
produces excellent performance in classifying 2-dimensional image data and the resulting 
training model results have relatively low computation. The accuracy training results of the 
Convolutional Neural Network (CNN) architecture that have been made show an accuracy 
of 98% and the results of the application training validation show an accuracy of 92.5%.}
		}