IDENTIFIKASI BUAH BERBASIS AROMA MENGGUNAKAN SENSOR GAS TERINTEGRASI KOLOM PARTISI
In this research, an electronic nose was made based on combination of gas sensor and partition column. In the incorporation of these two methods, it would be proved whether using data from separated compound that can used as an input to the artificial neural network would be able to identify the typ...
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| Main Authors: | , |
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| 格式: | Theses and Dissertations NonPeerReviewed |
| 出版: |
[Yogyakarta] : Universitas Gadjah Mada
2014
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| 主題: | |
| 在線閱讀: | https://repository.ugm.ac.id/129272/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=69662 |
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| 總結: | In this research, an electronic nose was made based on combination of gas
sensor and partition column. In the incorporation of these two methods, it would
be proved whether using data from separated compound that can used as an input
to the artificial neural network would be able to identify the type of the fruit that
based on the scent, therefore it was done the basic research in identify the type of
fruit based on the scent using gas sensor that integrated with partition column. The
purpose of this research was to identify the type of fruit based from the aroma
using an electronic nose based on the combination of gas sensor and partition
column. Moreover, this research was also purpose to make software based on
artificial neural network to identify the type of fruit that was built on a system.
The material was fruits, that had a sting aroma, in this case, durian,
jackfruit and pakel. For taking the data, it used the delphi serial comunication
program, whereas for identifying the fruit was used backpropagation neural
network. The length of data collection was set to be 5000 seconds, the heating
temperature in the column was set to be 60°C and the gas of fruit which get into
the column was set to be 90 seconds.
The artificial neural network that was used in this research has structure [3
100 5 3]. The neural network was built with biner sigmoid activation function.
The neural network used 3 input data from transformation result of difference
peak value with baseline. The result showed that the artificial neural network was
capable to identify the kind of fruits with accuracy 97.41 % from the variation of
the tested data.
Keywords: electronic nose, partition column, gas sensor, identification, fruit
aromas, artificial neural network |
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