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Optimization of Applied Detection Rate in the Simple Evolving Connectionist System Method for Classification of Images Containing Protein

Rahmad Syah, Al-Khowarizmi Al-Khowarizmi

Abstract


Digital image processing in general to makes images that appear converted to a function of light intensity represented in a two-dimensional plane. The function is a value that will be processed for classification so that the computer is able to recognize the image. Besides classification requires training and testing to produce a small error value and optimal algorithm. The problem of optimization is closely related to the principles and findings of science. Getting the smallest error value by calculating using MAPE for that MAPE calculation is done by using the Detection Rate formula to generalize knowledge in order to find the optimal model. Thus, the application of ANN is very suitable for optimizing classification using the Simple Evolving Connectionist System Method and as the result, the classification of images containing protein with test data is that the eggs work with optimal proof of achieving MAPE without modification of 0.1947% and MAPE which has been modified with the formula detection rate of 0.05554633%.

Keywords


Optimization; MAPE; Detection rate; SECoS; Classification

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References


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DOI: http://dx.doi.org/10.26555/jiteki.v7i1.20508

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