* Corresponding Author
Hide
Show all
Seleksi Fitur dengan Artificial Bee Colony untuk Optimasi Klasifikasi Data Teh menggunakan Support Vector Machine
https://doi.org/10.22146/ijeis.63902 Suhaila Suhaila(1*), Danang Lelono(2), Yunita Sari Sari(3)
(1) Program Studi Elektronika dan Instrumentasi; FMIPA UGM, Yogyakarta
(2) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(3) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
Tea quality can be recognized through the aroma it produces. Tea classification using e-nose generally only detects aroma using a general gas sensor. However, redundancy of sensor features can cause a decreasing in the system performance. Therefore we need a system that can select features so the classification performance becomes optimal. A software system of feature selection was built to optimize classification performance. Input data for the system is e-nose sensor response to 3 black tea qualities. The features are sensors on the e-nose instrument. Feature selection is implemented using wrapper approach, ABC algorithm is used for feature selection, then the selected features are evaluated by SVM classification. The results of the ABC-SVM system are then compared with the SVM only system. The results showed that from 12 e-nose sensors, sensors that most characterized black tea quality were TGS 2600, TGS 813, TGS 825, TGS 2602, TGS 2611, TGS 832, TGS 2612, TGS 2620 and TGS 822. Meanwhile, MQ-7, TGS 826 and TGS 2610 sensors are redundant in the system because the gas detected by the 3 sensors can be represented by other sensors. With the reduction in features to 9, the classification accuracy performance increased by 16.7%.
Keywords
Full Text:
PDFReferences
[1] N. Bhattacharyya, B. Tudu, R. Bandyopadhyay, M. Bhuya, and R. Mudi, “Aroma characterization of orthodox black tea with electronic nose,” IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, vol. B, pp. 427–430, 2004, doi: 10.1109/tencon.2004.1414623. https://ieeexplore.ieee.org/abstract/document/1414623 [Accessed: 23-Nov-2020]
[2] A. Dutta, B. Tudu, R. Bandyopadhyay, and N. Bhattacharyya, “Black tea quality evaluation using electronic nose: An artificial bee colony approach,” 2011 IEEE Recent Adv. Intell. Comput. Syst. RAICS 2011, no. 2, pp. 143–146, 2011, doi: 10.1109/RAICS.2011.6069290. https://ieeexplore.ieee.org/document/6069290 [Accessed: 23-Nov-2020]
[3] R. Banerjee, P. Chattopadhyay, B. Tudu, N. Bhattacharyya, and R. Bandyopadhyay, “Artificial flavor perception of black tea using fusion of electronic nose and tongue response: A Bayesian statistical approach,” J. Food Eng., vol. 142, pp. 87–93, 2014, doi: 10.1016/j.jfoodeng.2014.06.004. https://www.sciencedirect.com/science/article/abs/pii/S0260877418304102?casa_token=WUzxSYUrZmEAAAAA:oZMha9xUU95Oj0w_EJo2AEBAtURTPYyPrETeZ8Np3i6yfZddyNjGsn0rAoHsboAXHmaGpzZuaQ [Accessed: 14-Nov-2019]
[4] R. Dutta, E. L. Hines, J. W. Gardner, K. R. Kashwan, and M. Bhuyan, “Tea quality prediction using a tin oxide-based electronic nose: An artificial intelligence approach,” Sensors Actuators, B Chem., vol. 94, no. 2, pp. 228–237, 2003, doi: 10.1016/S0925-4005(03)00367-8. https://www.researchgate.net/publication/223495802_Tea_Quality_Prediction_Using_a_Tin_Oxide-based_Electronic_Nose_an_Artificial_Intelligence_Approach [Accessed: 26-Des-2020]
[5] B. Santosa and D. R. Hanum, “Studi komparasi metode klasifikasi dua kelas,” Pros. Semin. Nas. Manaj. Teknol. V, 2007. https://adoc.pub/studi-komparasi-metode-klasifikasi-dua-kelas.html [Accessed: 27-Sep-2019]
[6] Q. Chen, J. Zhao, Z. Chen, H. Lin, and D. A. Zhao, “Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools,” Sensors Actuators, B Chem., vol. 159, no. 1, pp. 294–300, 2011, doi: 10.1016/j.snb.2011.07.009. https://www.sciencedirect.com/science/article/abs/pii/S0925400511006393 [Accessed: 4-Okt-2019]
[7] S. Jenicka and A. Suruliandi, “Comparative study of classification algorithms with modified multivariate local binary pattern texture model on remotely sensed images,” Int. Conf. Recent Trends Inf. Technol. ICRTIT 2011, pp. 848–852, 2011, doi: 10.1109/ICRTIT.2011.5972312. https://ieeexplore.ieee.org/abstract/document/5972312 [Accessed: 27-Sep-2019]
[8] R. Prakash, V. P. Tharun, and S. Renuga Devi, “A Comparative Study of Various Classification Techniques to Determine Water Quality,” Proc. Int. Conf. Inven. Commun. Comput. Technol. ICICCT 2018, no. Icicct, pp. 1501–1506, 2018, doi: 10.1109/ICICCT.2018.8473168. https://ieeexplore.ieee.org/document/8473168 [Accessed: 17-Sep-2019]
[9] Y. T. Liu and K. T. Tang, “A Minimum Distance Inlier Probability (MDIP) Feature Selection Method to Improve Gas Classification for Electronic Nose Systems,” IEEE Access, vol. 8, pp. 133928–133935, 2020, doi: 10.1109/ACCESS.2020.3010788. https://ieeexplore.ieee.org/abstract/document/9145749 [Accessed: 27-Des-2020]
[10] Y. Lu, I. Cohen, X. S. Zhou, and Q. Tian, “Feature selection using principal feature analysis,” in Proceedings of the 15th international conference on Multimedia - MULTIMEDIA ’07, 2007, p. 301, doi: 10.1145/1291233.1291297. https://ieeexplore.ieee.org/document/5640135 [Accessed: 4-Nov-2019]
[11] D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm,” J. Glob. Optim., vol. 39, no. 3, pp. 459–471, 2007, doi: 10.1007/s10898-007-9149-x. https://www.researchgate.net/publication/225392029_A_powerful_and_efficient_algorithm_for_numerical_function_optimization_Artificial_bee_colony_ABC_algorithm [Accessed: 5-Apr-2019]
[12] B. Basu and G. K. Mahanti, “A comparative study of modified particle swarm optimization, differential evolution and artificial bees colony optimization in synthesis of circular array,” ICPCES 2010 - Int. Conf. Power, Control Embed. Syst., pp. 1–5, 2010, doi: 10.1109/ICPCES.2010.5698614. https://www.researchgate.net/publication/251987264_A_comparative_study_of_Modified_Particle_Swarm_Optimization_Differential_Evolution_and_Artificial_Bees_Colony_optimization_in_synthesis_of_circular_array [Accessed: 10-Sep-2019]
[13] M. Meguellati, F. Srairi, F. Djeffal, and L. Saidi, “Performance analysis of swimming microrobot using GA, ABC and PSO based-optimization techniques,” 2015 4th Int. Conf. Syst. Control. ICSC 2015, pp. 310–314, 2015, doi: 10.1109/ICoSC.2015.7153277. https://ieeexplore.ieee.org/document/7153277 [Accessed: 13-Sep-2019]
[14] D. Lelono, “Pengembangan Instrumentasi Sistem Electronic Nose untuk Uji Teh Hitam Lokal. Universitas Gadjah Mada,” Universitas Gadjah Mada, 2017.
[15] M. Monirul Kabir, M. Monirul Islam, and K. Murase, “A new wrapper feature selection approach using neural network,” Neurocomputing, vol. 73, no. 16–18, pp. 3273–3283, 2010, doi: 10.1016/j.neucom.2010.04.003. https://www.sciencedirect.com/science/article/abs/pii/S0925231210001979 [Accessed: 11-Des-2020]
DOI: https://doi.org/10.22146/ijeis.63902
Article Metrics
Abstract views : 1364
| 
views : 1471
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1