期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2016
卷号：14
期号：2
页码：778-783
DOI：10.12928/telkomnika.v14i2.2469
语种：English
出版社：Universitas Ahmad Dahlan
摘要：Tuna and mackerel are important fish in Indonesia that have great demand in the community and contain good nutrients for health. Many of the processed products have been faked including processed fish, by replacing the content of products that have high sales value to other lower price one. For ensuring food safety, fraudulent should be prevented by identifying the content of refined product. In this research, we implemented support vector machine (SVM), one of the popular methods in machine learning, to yield a model for identifying the content of refined product based on DNA barcode sequences. The feature extraction of DNA barcode Sequences was conducted by calculating k-mers frequency of each sequences. In this study, we used trinucleotide (3-mers) and tetranucleotide (4-mers). These features were inputted to SVM to classify and identify whether the DNA barcode sequences belong to the class of tuna, mackerel, or other fish. The evaluation results showed model SVM was able to perform identification with the accuracy 88%.
其他摘要：Tuna and mackerel are important fish in Indonesia that have great demand in the community and contain good nutrients for health. Many of the processed products have been faked including processed fish, by replacing the content of products that have high sales value to other lower price one. For ensuring food safety, fraudulent should be prevented by identifying the content of refined product. In this research, we implemented support vector machine (SVM), one of the popular methods in machine learning, to yield a model for identifying the content of refined product based on DNA barcode sequences. The feature extraction of DNA barcode Sequences was conducted by calculating k-mers frequency of each sequences. In this study, we used trinucleotide (3-mers) and tetranucleotide (4-mers). These features were inputted to SVM to classify and identify whether the DNA barcode sequences belong to the class of tuna, mackerel, or other fish. The evaluation results showed model SVM was able to perform identification with the accuracy 88%.
关键词：DNA Barcode; food safety; machine learning; support vector machine
