Robot Classifying of Gas using Support Vector Machine Method

Moch Fachri, Nyayu Latifah Husni, Ekawati Prihatini

Abstract


Fires often happen in the environment of industrial chemical plant caused by harmful gases. To minimize the incident that can be triggered by the gas it takes a tool capable of classifying gases are in the environment industry. The purpose of this research is to know the success in classifying gas on a fire that was triggered by the dangerous gases. We offer solutions in design and build a mobile robot that can classify objects contain hazardous gases by using the method of pattern recognition, the age of the SVM is still relatively young. Nevertheless, the advantages of SVM compared to another method lies in its ability to find the best hyperplane that separates the two class. Based on the results of testing data can classify SVM managed in accordance with the class. The degree of accuracy achieved SVM in classifying reached 86.66 %.


Keywords


Support Vector Machine; Classification; Hyperplane; Sensor TGS

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References


Burges, C. J. C. (1998). A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2(2), 121–167. https://doi.org/10.1023/A:1009715923555

Byun, H., & Lee, S.-W. (2003). A survey on pattern recognition applications of support vector machines. International Journal of Pattern Recognition and Artificial Intelligence, 17(03), 459–486. https://doi.org/10.1142/S0218001403002460

Dinambar, B. P., Sari, D. P., & Irdayanti, Y. (2017). Analysis of The Measurement of PH Levels and Levels of Water Clarity on The Ship’s Robot. VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro, 2(2), 133. https://doi.org/10.30870/volt.v2i2.1929

F.A Novianti, S. . P. (2012). Analisis Diagnosis Pasien Kanker Payudara Menggunakan Regresi Logistik dan Support Vector, 1(1).

Hidayatullah, T. (2014). Kajian Komparasi Penerapan Algoritma Support Vector Machine (SVM) dan Multilayer Perceptron (MLP) dalam Prediksi Indeks Saham Sektor Perbankan.

Li, J.-G., Meng, Q.-H., Wang, Y., & Zeng, M. (2010). Single odor source declaration in outdoor time-variant airflow environments. In 2010 IEEE International Conference on Robotics and Biomimetics (pp. 143–148). IEEE. https://doi.org/10.1109/ROBIO.2010.5723317

Lilienthal, A., Ulmer, H., Frohlich, H., Stutzle, A., Werner, F., & Zell, A. (2004). Gas source declaration with a mobile robot. In IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA ’04. 2004 (p. 1430–1435 Vol.2). IEEE. https://doi.org/10.1109/ROBOT.2004.1308025

Loutfi, A., Coradeschi, S., Karlsson, L., & Broxvall, M. (2005). Putting olfaction into action: using an electronic nose on a multi-sensing mobile robot. In 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566) (Vol. 1, pp. 337–342). IEEE. https://doi.org/10.1109/IROS.2004.1389374

Nugroho, A. S., Witarto, A. B., & Handoko, D. (2003). Support Vector Machine-Teori dan Aplikasinya dalam Bioinformatika 1. Retrieved from http://asnugroho.net

Oktarina, Y., Nawawi, M., & Tulak, W. G. (2017). Analysis of The Sensor Line on Line Follower Robot as an Alternative Transport The Tub Trash in The Shopping Center. VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro, 2(2), 101. https://doi.org/10.30870/volt.v2i2.1859

Pratama, Y. W., Dewi, T., & Oktarina, Y. (2017). Analysis and Design of Obstacle Avoidance on Robot Detection of Pipe Cracked. VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro, 2(2), 167. https://doi.org/10.30870/volt.v2i2.2044

Redden, E. S., Pettitt, R. A., Carstens, C. B., & Elliott, L. R. (2008). Scalability of Robotic Displays: Display Size Investigation. Retrieved from https://apps.dtic.mil/docs/citations/ADA481586

Rendyansyah. (2015). Implementasi Kendali Logika Fuzzy dan Pengenalan Pola Support Vector Machine Pada Olfactory Arm Mobile Robot untuk Identifikasi Gas.

Suryadi, U. T. (2015). Komparasi Support Vector Machine dan Neural Network untuk Prediksi Kelulusan Sertifikasi Benih Kentang. Seminar Nasional Informatika (SEMNASIF), 1(1). Retrieved from http://jurnal.upnyk.ac.id/index.php/semnasif/article/view/1397

Trincavelli, M., & Loutfi, A. (2010). Feature selection for gas identification with a mobile robot. In 2010 IEEE International Conference on Robotics and Automation (pp. 2852–2857). IEEE. https://doi.org/10.1109/ROBOT.2010.5509617




DOI: http://dx.doi.org/10.30870/volt.v3i1.2010

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