PERBANDINGAN METODE DETEKSI TEPI DALAM KASUS PENGENALAN POLA GOLONGAN DARAH MENGGUNAKAN ALGORITMA RUNUT BALIK ( BACK PROPAGATION )

AGUNG NUR HIDAYAT, NIM. 08650062 (2012) PERBANDINGAN METODE DETEKSI TEPI DALAM KASUS PENGENALAN POLA GOLONGAN DARAH MENGGUNAKAN ALGORITMA RUNUT BALIK ( BACK PROPAGATION ). Skripsi thesis, UIN SUNAN KALIJAGA.

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Abstract

Before performing a blood transfusion someone needs to know their blood type. For regular medical checks blood type would be very easy to distinguish whether the results of the test showed blood group A, B, AB or O. The process of blood type checking the depends on how much accuracy medical blood checks. To keep the results of the checks can be valid, needs to be a system that can help people to recognize the type of blood type. A way to recognize blood type using the computer by using pattern recognition methods and training of each blood type characteristics through the image. Domain from this research is the image of blood checks. This image obtained from scanning checks of blood groups from PMI Yogyakarta as many as 54 images for training and recognition processes. The image is used with extension .bmp with the size of 400 x 200 pixels. Before image recognition, preprocessing is performed which is conversion of color images to grayscale images. The next process is edge detection using sobel operator or prewitt operator. The use of two operators aims to determine the optimal operator transform to the case of blood recognition. After the process of edge detection, converted to a binary image in order to be processed by feature extraction. The final step is the application of artificial neural network with backpropagation algorithm for the hidden layer activation function is the sigmoid activation for bipolar and output is linear. Architectural optimal neural network are the three hidden layers with each hidden layer has three node. The optimal value for the mean squared error parameter is 1e-1 or 0.1, epoch 1000 and learning rate 0.01. In this research, sobel operator is better than the prewitt operator in making the recognition of blood type. When viewed from the difference between the process, the prewitt operator slightly faster than Sobel operator with a gap of 0.000052 sec. From 39 training data and 14 test data percent of data obtained successful results of the recognition of blood group type of 92,86%. Keywords: Backpropagation, Blood, Prewitt, Sobel

Item Type: Thesis (Skripsi)
Subjects: Tehnik Informatika
Divisions: Fakultas Sains dan Teknologi > Teknik Informatika (S1)
Depositing User: Miftahul Ulum [IT Staff]
Date Deposited: 29 May 2013 17:17
Last Modified: 07 Mar 2016 11:02
URI: http://digilib.uin-suka.ac.id/id/eprint/7912

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