A gemstone is a mineral stone that be formed from the result of geological processes and has a hardness above 7 Mohs. Nowadays, gemstones have become famous in Indonesian society. Many facts concerning the business of gemstone rings, including encouragement by the the central government for the gemstone souvenirs that are given to State guests by the President of Indonesia, gemstone contests and gemstone exhibitions have all contributed to pique the interest of researchers in the subject. This current research is undertaken to create a system that is able to identify three kinds of gemstone, namely, Ruby, Sapphire and Emerald using the Hue Saturation Value (HSV) colour space, image processing techniques and an Artificial Neural Network (ANN) back propagation algorithm by learning from examples. The hue from the HSV colour space of a gemstone will be used by the system for training. In the image processing, the system will crop, resize, convert from RGB to HSV, obtain a Hue colour and extract the colour as a 30x3 matrix. The extracted result will be used to train an ANN consisting of three input layers, three hidden layers and one output layer with targets that have been pre-determined. The results of the tests showed a degree of accuracy of 90.66% with 5 times of training and 25 times of testing on any type of gemstone. The result shows that using Artificial Neural Network Back Propagation in identification gemstone types is success, because the accuracy system has a highest percentage.
Movement change beyond the duration of time and the variations of object appearance becomes an interesting topic for research in computer vision. Object behavior can be recognized through movement change on video. During the recognition of object behavior, the target and the trace of an object in a video must be determined in the sequence of frames. To date, the existence of object on a video has been widely used in different areas such as supervision, robotics, agriculture, health, sports, education, and traffic. This research focuses on the field of education by recognizing the movement of Quantum Maki Quran memorization through a video. The purpose of this study is to enhance the existing computer vision technique in detecting the Quantum Maki Quran memorization movement on a video. It combines the Background Subtraction method and Artificial Neural Networks; and evaluates the combination to optimize the system accuracy. Background Subtraction is used as object detection method and Back propagation in Artificial Neural Networks is used as object classification. Nine videos are obtained by three different volunteers. These nine videos are divided into six training and three testing data. The experimental result shows that the percentage of accuracy system is 91.67%. It can be concluded that there are several factors influencing the accuracy, such as video capturing factors, video improvements, the models, feature extraction and parameter definitions during the Artificial Neural Networks training.
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