GDL

Journal from gdlhub / 2018-07-26 09:16:34
Oleh : Anifatul Faricha, Suwito Suwito, M. Rivai, M.A. Nanda, Djoko Purwanto, Rizki Anhar R.P., Telkomnika
Dibuat : 2018-07-26, dengan 1 file

Keyword : Acoustic features; gas chromatography (GC); hyperplane; support vector machine (SVM); surface acoustic wave (SAW)
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/7127
Sumber pengambilan dokumen : WEB

Electronic nose system using a combination of Gas Chromatography Column and a Surface Acoustic Wave (SAW) sensor has been developed to recognize odors. Gas samples including methanol, acetonitrile, and benzene were used for system performance measurement. Each gas sample generated a specific acoustic signal data in the form of a frequency change recorded by the SAW sensor. Then, the acoustic signal data would be analyzed to obtain the acoustic features, i.e. the peak amplitude, the negative slope, the positive slope, and the length. The Support Vector Machine (SVM) method using the acoustic feature as its input parameters were applied to classify the gas sample. Radial Basis Function was used to build optimal hyperplane model. After training process, the system can recognize the type of gas with an accuracy of 93.3%.

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