Klasifikasi Citra Jenis Fosil Mikropaleontologi Foraminifera
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Institusion
Universitas Islam Riau
Author
Utama, Wildan
Subject
QA76 Computer software
Datestamp
2022-03-15 03:43:35
Abstract :
The Foraminifera micropaleontology fossils are planktonic microfossils, family globigerinoides with the genus Orbulina. Currently, in determining this type of fossil, geological students are still using the manual way, patting the fossils they find or they were thoroughly examined. Therefore it is necessary a system that can classify fossils with the processing of image and artificial neural network. The fossil image is processed using Matlab. Digital image processing is used to extract texture features from the fossil image. The artificial neural network is used for fossil classification. The research used 60 training data and 24 testing data in the divide into 3 classes, namely the class of Orbulina Bilobata, Orbulina saturalis, Orbulina Universa. The parameters used for the input of the neural network are mean values, entropy, Variance, skewness, kurtosis. Thus the system is able to classify the fossil type with a success rate of 87.5% of 24 testing data.
The Foraminifera micropaleontology fossils are planktonic microfossils, family globigerinoides with the genus Orbulina. Currently, in determining this type of fossil, geological students are still using the manual way, patting the fossils they find or they were thoroughly examined. Therefore it is necessary a system that can classify fossils with the processing of image and artificial neural network. The fossil image is processed using Matlab. Digital image processing is used to extract texture features from the fossil image. The artificial neural network is used for fossil classification. The research used 60 training data and 24 testing data in the divide into 3 classes, namely the class of Orbulina Bilobata, Orbulina saturalis, Orbulina Universa. The parameters used for the input of the neural network are mean values, entropy, Variance, skewness, kurtosis. Thus the system is able to classify the fossil type with a success rate of 87.5% of 24 testing data.
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