Analisis Respons Sensor Electroni Tongue terhadap Sampel Ganja menggunakan Support Vector Machine

https://doi.org/10.22146/ijeis.49173

Wikan Haryo Rahmantyo(1*), Danang Lelono(2)

(1) Program Studi Elektronika dan Instrumentasi, DIKE, FMIPA, UGM, Yogyakarta
(2) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(*) Corresponding Author

Abstract


Electronic tongue sensors consisting of 16 sensor array made of TOMA and OA lipids that have been used to classify samples of pure cannabis, cannabis mixed with tea and cannabis mixed with tobacco does not involve the feature selection technique so that a lot of duplicated data is generated from data sampling. Feature selection is performed using PCA. Data analysis resulted in loading values shows the contribution of each sensor, and the similarity in sensor performance in characterizing samples, then analyzed using the correlation test so that the sensors that produce redundant information are known. Validation is performed using the SVM method and the classification performance is compared to the original sensor.

The sensor optimization produces a subset of features with 6 sensors (Sensor 7, Sensor 10, Sensor 12, Sensors 13, Sensor 14 and Sensor 15) in the cannabis-tea sample test and a feature subset with 3 sensors (Sensor 3, Sensor 7 and Sensor 14) in the cannabis-tobacco sample test. Sensor optimization that has been done produced classification accuracy by 100% and shorten the running time by a difference of 0.578 microseconds in the test of cannabis-tea samples and a difference of 1.696 microseconds in the test of cannabis-tobacco samples.


Keywords


electronic tongue; support vector machine; PCA; sensor optimization; feature selection

Full Text:

PDF


References

[1] D. Putri and T. Blickman, “Cannabis in Indonesia,” no. January, pp. 1–24, 2016. https://www.tni.org/files/publication-downloads/dpb_44_13012016_map_web.pdf

[2] C. M. Andre, J.-F. Hausman, and G. Guerriero, “Cannabis sativa: The Plant of the Thousand and One Molecules,” Front. Plant Sci., vol. 7, no. February, 2016. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4740396/

[3] J. Rabbani, “Rancang Bangun Larik Sensor Rasa berbasis Campuran Lipid TOMA dan OA untuk Klasifikasi Ganja,” Universitas Gajah Mada, 2017. http://etd.repository.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=112287&obyek_id=4

[4] I. Tazi, “Studi dan Pengembangan Lidah Elektronik berbasis 16 Multikanal Sensor Membran Lipid,” Universitas Gadjah Mada, 2017. http://etd.repository.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=128640&obyek_id=4

[5] R. E. Saputro, “Pengembangan Larik Sensor E-Tongue berbasis Lipid OA (Oleic Acid) dan TOMA (Trioctyl Methyl Ammonium Chloride) untuk Identifikasi Ganja dan Campurannya,” Universitas Gadjah Mada, 2017. http://etd.repository.ugm.ac.id/downloadfile/112300/potongan/S1-2017-331380-title.pdf

[6] I. Tazi, K. Triyana, D. Siswanta, A. C. A. Veloso, A. M. Peres, and L. G. Dias, “Dairy products discrimination according to the milk type using an electrochemical multisensor device coupled with chemometric tools,” J. Food Meas. Charact., vol. 12, no. 4, pp. 2385–2393, 2018. https://www.researchgate.net/publication/325776120_Dairy_products_discrimination_according_to_the_milk_type_using_an_electrochemical_multisensor_device_coupled_with_chemometric_tools

[7] G. Casella, S. Fienberg, and I. Olkin, An Introduction to Statistical Learning with Applications in R. Springer Texts in Statistics, 2015. https://www.springer.com/gp/book/9781461471370

[8] J. P. Lander, R for Everyone Advanced Analytics and Graphics Second ED., vol. 7. Pearson Education Inc., 2017.

[9] H. Sun et al., “Sensor Array Optimization of Electronic Nose for Detection of Bacteria in Wound Infection,” vol. 64, no. 9, pp. 7350–7358, 2017. https://ieeexplore.ieee.org/document/7902191

[10] E. S. Wahyuni, “Klasifikasi SVM(Support Vector Machine) dan Kombinasi Seleksi Fitur pada Diagnosis Kanker Payudara,” Universitas Gadjah Mada, 2014. http://etd.repository.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=74052

[11] N. M. Makarova and E. G. Kulapina, “A new potentiometric sensors for determination of sodium alkylsulfates,” in Procedia Engineering, 2014, vol. 87, pp. 284–287. https://www.sciencedirect.com/science/article/pii/S1877705814027854



DOI: https://doi.org/10.22146/ijeis.49173

Article Metrics

Abstract views : 4808 | views : 3388

Refbacks

  • There are currently no refbacks.




Copyright (c) 2019 IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



Copyright of :
IJEIS (Indonesian Journal of Electronics and Instrumentations Systems)
ISSN 2088-3714 (print); ISSN 2460-7681 (online)
is a scientific journal the results of Electronics
and Instrumentations Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijeis.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijeis



View My Stats1
View My Stats2