ABSTRACT

Hartanto, A Palm Hand Identification System (supervised by Rhiza S. Sadjad and Zahir Zainuddin).

	The main goal of this research is to make a comparison study of the use of 3 (three) devices, namely a digital camera, a scanner and a web-camera as the image sources of a palm hand identification system with the Neural Network Back Propagation (NNBP) and the Fuzzy Neural Network (FNN)  algorithms. The success rate of identification is also considered under some variation of the colors.
	As many as <berapa banyak> samples of palm hand images created using the three devices have been taken, <berapa persen> of those are used for the training process of the NNBP algorithm, and the rest are used for testing the success rate of both the NNBP and the FNN algorithms, and also for testing the success rate of identification after modifying the images by some color variations.
	The best success rate is the identification using the scanner, while the worst is the <web-camera atau digital camera?>. The FNN algorithm performs better than the NNBP algorithm. The success rates of the NNBP algorithm are 78.13%, <berapa persen> and <berapa persen> for the scanner, the web-camera and digital camera respectively. These are improved significantly up to 87.50%, <berapa persen> and <berapa persen> when the FNN algorithm is used. When the color of the scanner images is varied, the success rate decreases down to 27.78% and 35.42% for the NNBP algorithm and FNN algorithm respectively. The color variation is also similarly affecting the identification results using the images taken by the digital camera and the web-camera. This shows that the palm hand identification system developed in this research is very sensitive to the color variation of the images, regardless of the devices used.