Edge detection is the first step to cover information in the image. The edges characterize the boundaries of objects and therefore edges are useful for the process of segmentation and identification in the image. The purpose of edge detection is to increase the appearance of the boundary line of the object in the image. The sobel method is a method that uses two kernels measuring 3x3 pixels for gradient calculations so that the estimate gradient is right in the middle of the window. Digital image processing aims to manipulate image data and analyze an image with the help of a computer. Matlab is made to facilitate the use of two collections of subroutines in the fortran library, linpack and eispack, in handling matrix computing, and develops into an interactive system as a programming language. Experimental results from the input image research, namely the flower image have different MSE values because each input image has a different pixel value

-The fingerprint is one of the biometric methods which is can be used in the education field. Attendance monitoring system using fingerprint will make the leaders easily to monitor the attendance of the lecturer and to make decisions. The fingerprint attendance is used to easy students detecting quickly and accurately the lecturer in the classroom. This system aims to provide the lecturer status information, entry or exit for teaching when every lecturer performs a fingerprint scanning. This study uses rapid application development (RAD) method to develop attendance system and involves the lecturer who have a teaching schedule in the current semester. To test the system, the lecturer was required to record the fingerprint in fingerprint machine. The result shows that the application of lecturer attendance in real time web-based can be a system provides the lecturers attendance information effectively and efficiently.

Electromyography (EMG) signal is an myoelectric signal in the muscle layer. It occurs caused by contraction and relaxation muscle activity. This article provide numerical study of the classifying the electromyography signal for wrist movement combined with open and grasping finger flexor. The EMG signal has recorded using a device called electromyography. It has acquired by attaching an surface electrode in the skin then the electrode was capturing the raw signal. The volunteer involved were six where each volunteer has ten datasets the EMG signal. The surface electrode are sticked in the lower arm muscle. The EMG raw signal was processed using zero-mean normalization. The feature extraction method is root mean square (rms), mean absolute value (mav), variance (var), and standard deviation (std). This EMG signal has been classified by naïve bayes classifier. Training and testing data was using 5-cross validation. The result indicates that the classification accuracy for classifying the EMG signal for wrist movement combined open finger flexor (OFF) and grasping finger flexor (GFF) is 70% and 75% respectively. Therefore, the EMG signal can be applied for identificating of muscle disorder, prostheses hand and biometric system.

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