ARTIFICIAL NEURAL NETWORK APPROACH FOR THE IDENTIFICATION OF CLOVE BUDS ORIGIN BASED ON METABOLITES COMPOSITION
This paper examines the use of an artificial neural network approach in identifying the origin of clove buds based on metabolites composition. Generally, large data sets are critical for an accurate identification. Machine learning with large data sets lead to a precise identification based on origi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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CTU Central Library
2020-11-01
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| Series: | Acta Polytechnica |
| Subjects: | |
| Online Access: | https://ojs.cvut.cz/ojs/index.php/ap/article/view/6132 |
| _version_ | 1818256306654412800 |
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| author | Rustam Agus Yodi Gunawan Made Tri Ari Penia Kresnowati |
| author_facet | Rustam Agus Yodi Gunawan Made Tri Ari Penia Kresnowati |
| author_sort | Rustam |
| collection | DOAJ |
| description | This paper examines the use of an artificial neural network approach in identifying the origin of clove buds based on metabolites composition. Generally, large data sets are critical for an accurate identification. Machine learning with large data sets lead to a precise identification based on origins. However, clove buds uses small data sets due to the lack of metabolites composition and their high cost of extraction. The results show that backpropagation and resilient propagation with one and two hidden layers identifies the clove buds origin accurately. The backpropagation with one hidden layer offers 99.91% and 99.47% for training and testing data sets, respectively. The resilient propagation with two hidden layers offers 99.96% and 97.89% accuracy for training and testing data sets, respectively. |
| first_indexed | 2024-12-12T17:25:40Z |
| format | Article |
| id | doaj.art-1b49441bf4aa45b1ac1c2f65d6b11e29 |
| institution | Directory Open Access Journal |
| issn | 1210-2709 1805-2363 |
| language | English |
| last_indexed | 2024-12-12T17:25:40Z |
| publishDate | 2020-11-01 |
| publisher | CTU Central Library |
| record_format | Article |
| series | Acta Polytechnica |
| spelling | doaj.art-1b49441bf4aa45b1ac1c2f65d6b11e292022-12-22T00:17:32ZengCTU Central LibraryActa Polytechnica1210-27091805-23632020-11-0160544044710.14311/AP.2020.60.04403328ARTIFICIAL NEURAL NETWORK APPROACH FOR THE IDENTIFICATION OF CLOVE BUDS ORIGIN BASED ON METABOLITES COMPOSITIONRustam0https://orcid.org/0000-0001-8331-5793Agus Yodi Gunawan1Made Tri Ari Penia Kresnowati2Institut Teknologi Bandung, Faculty of Mathematics and Natural Sciences, Industrial and Financial Mathematics Research Group, Jl. Ganesha 10, 40132 Bandung, IndonesiaInstitut Teknologi Bandung, Faculty of Mathematics and Natural Sciences, Industrial and Financial Mathematics Research Group, Jl. Ganesha 10, 40132 Bandung, IndonesiaInstitut Teknologi Bandung, Faculty of Industrial Technology, Food and Biomass Processing Technology Research Group, Jl. Ganesha 10, 40132 Bandung, IndonesiaThis paper examines the use of an artificial neural network approach in identifying the origin of clove buds based on metabolites composition. Generally, large data sets are critical for an accurate identification. Machine learning with large data sets lead to a precise identification based on origins. However, clove buds uses small data sets due to the lack of metabolites composition and their high cost of extraction. The results show that backpropagation and resilient propagation with one and two hidden layers identifies the clove buds origin accurately. The backpropagation with one hidden layer offers 99.91% and 99.47% for training and testing data sets, respectively. The resilient propagation with two hidden layers offers 99.96% and 97.89% accuracy for training and testing data sets, respectively.https://ojs.cvut.cz/ojs/index.php/ap/article/view/6132artificial neural networksbackpropagationresilient propagationclove buds |
| spellingShingle | Rustam Agus Yodi Gunawan Made Tri Ari Penia Kresnowati ARTIFICIAL NEURAL NETWORK APPROACH FOR THE IDENTIFICATION OF CLOVE BUDS ORIGIN BASED ON METABOLITES COMPOSITION Acta Polytechnica artificial neural networks backpropagation resilient propagation clove buds |
| title | ARTIFICIAL NEURAL NETWORK APPROACH FOR THE IDENTIFICATION OF CLOVE BUDS ORIGIN BASED ON METABOLITES COMPOSITION |
| title_full | ARTIFICIAL NEURAL NETWORK APPROACH FOR THE IDENTIFICATION OF CLOVE BUDS ORIGIN BASED ON METABOLITES COMPOSITION |
| title_fullStr | ARTIFICIAL NEURAL NETWORK APPROACH FOR THE IDENTIFICATION OF CLOVE BUDS ORIGIN BASED ON METABOLITES COMPOSITION |
| title_full_unstemmed | ARTIFICIAL NEURAL NETWORK APPROACH FOR THE IDENTIFICATION OF CLOVE BUDS ORIGIN BASED ON METABOLITES COMPOSITION |
| title_short | ARTIFICIAL NEURAL NETWORK APPROACH FOR THE IDENTIFICATION OF CLOVE BUDS ORIGIN BASED ON METABOLITES COMPOSITION |
| title_sort | artificial neural network approach for the identification of clove buds origin based on metabolites composition |
| topic | artificial neural networks backpropagation resilient propagation clove buds |
| url | https://ojs.cvut.cz/ojs/index.php/ap/article/view/6132 |
| work_keys_str_mv | AT rustam artificialneuralnetworkapproachfortheidentificationofclovebudsoriginbasedonmetabolitescomposition AT agusyodigunawan artificialneuralnetworkapproachfortheidentificationofclovebudsoriginbasedonmetabolitescomposition AT madetriaripeniakresnowati artificialneuralnetworkapproachfortheidentificationofclovebudsoriginbasedonmetabolitescomposition |