Implementasi Jaringan Syaraf Tiruan Bidirectional Associative Memory Untuk Pengidentifikasian Telapak Tangan Manusia (Studi Kasus: Mahasiswa S1 Ilmu Komputer Usu Stambuk 2010 Kom A)
IMPLEMENTATION OF NEURAL NETWORK BIDIRECTIONAL ASSOCIATIVE
MEMORY FOR PALM HUMAN PATTERN RECOGNITION
ABSTRACT
Identity recognition system can be implemented by biometric system. Biometric system has
better security grade than traditional identity recognition system because biometric system
cannot be duplicated, stolen, lost, and forgotten. Palm is one of biometric system which
pattern can be used to recognize identity. This Pattern can be identified by Artificial Neural
Network (ANN). ANN is a computational system where the architecture and operation
inspired by knowledge about biological neural cells in the brain. One method in artificial
neural networks is Bidirectional Associative Memory (BAM) which is a type of neural
network with hetero-associative memory by using two layers, that is the input layer and
output layer which has the ability to identify pattern after reaching a stable state. In this
research, the pattern used as the input pattern is the image of the palm of the right hand with
the characteristic geometry and principle lines taken by scanner to produce image of
transverse right palm, then processed to obtain a regular image with the size 200 x 300 pixels
and converted to obtain bipolar patterns with values 1 and -1. This pattern will be used for
training to produce a weight matrix which serve as a benchmark for testing the right palm
pattern. This research has two tests, first testing on the right palm image pattern that was
used for training with the result of identification is 100% and second testing on the image of
right palm pattern not previously scanned with the identification result of 52,5%. The
conclusion of the research the Bidirectional Associative Memory method is able to identify
pattern by utilizing characteristic geometry and principle lines of the palm.
Keywords: Artificial Neural Network, Bidirectional Associative Memory, Palms pattern,
Weight matrix, Bipolar patterns.
Universitas Sumatera Utara
MEMORY FOR PALM HUMAN PATTERN RECOGNITION
ABSTRACT
Identity recognition system can be implemented by biometric system. Biometric system has
better security grade than traditional identity recognition system because biometric system
cannot be duplicated, stolen, lost, and forgotten. Palm is one of biometric system which
pattern can be used to recognize identity. This Pattern can be identified by Artificial Neural
Network (ANN). ANN is a computational system where the architecture and operation
inspired by knowledge about biological neural cells in the brain. One method in artificial
neural networks is Bidirectional Associative Memory (BAM) which is a type of neural
network with hetero-associative memory by using two layers, that is the input layer and
output layer which has the ability to identify pattern after reaching a stable state. In this
research, the pattern used as the input pattern is the image of the palm of the right hand with
the characteristic geometry and principle lines taken by scanner to produce image of
transverse right palm, then processed to obtain a regular image with the size 200 x 300 pixels
and converted to obtain bipolar patterns with values 1 and -1. This pattern will be used for
training to produce a weight matrix which serve as a benchmark for testing the right palm
pattern. This research has two tests, first testing on the right palm image pattern that was
used for training with the result of identification is 100% and second testing on the image of
right palm pattern not previously scanned with the identification result of 52,5%. The
conclusion of the research the Bidirectional Associative Memory method is able to identify
pattern by utilizing characteristic geometry and principle lines of the palm.
Keywords: Artificial Neural Network, Bidirectional Associative Memory, Palms pattern,
Weight matrix, Bipolar patterns.
Universitas Sumatera Utara