| Summary: | Peanut is one of the important commodities due to its high economic
value, especially as foodstuff. Before processed, peanut should have a good
physical quality seed with appropriate quality standards that has been set. Peanut�s
quality classification is still conducted manually by comparing the proportion of
its type based on visual observation. This method has a weakness that the result
will be qualitative and subjective. Thus the stage classification can be
inconsistent. Therefore, it is necessary to design a classification system to
determine peanut�s physical quality which is quantitative and objective at the
operator level, one of which is using image processing method and artificial
neural networks. This study aims to conduct a series of experiments on samples of
peanut seeds of which their quality level will later be classified based on
Indonesian National Standard (SNI) 01-3921-1995.
The stage of peanut image processing begins with a black box which is
equipped with a webcam Genius I Slim 2020AF. Peanut�s image consist of
normal, wrinkled, cracked, damaged, and other color type. Image processing is
performed to analyze the color and texture of the image that will be used as the
physical parameters of quality classification. Color and texture parameters used
are red, green, blue, entropy, contrast, homogenity, and energy. Architecture of
artificial neural network using feedforward backpropagation algorithm. Next there
should be an identification of sample types using artificial neural network with
240 training samples and 60 testing samples. Physical quality classification based
on the proportion of each type of peanut is conducted using 204 trial validation
samples of peanut seeds.
Image processing stage shows that it�s only color parameter that is used to
determine peanut�s physical quality classification. The result of the study on trial
validation samples displayed in the GUI (Graphical User Interface) shows that
samples data classified into physical quality III with 91,67% accuracy rate with
the proportion of peanut�s normal type 53,43%, wrinkled type 21,57%, cracked
type 15,69%, damaged type 5,39%, and other color type 3,92%.
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