Aplikasi SVM Classifier dalam Pengenalan Target IR (infrared)
DOI:
https://doi.org/10.31102/zeta.2015.1.1.6-9Keywords:
SVM classifier, IR, pengenalan targetAbstract
Dalam tulisan ini, support vector machine diusulkan dalam pengenalan target IR. Metode grid digunakan untuk memilih parameter yang tepat dari SVM untuk menghindari over-fitting yang disebabkan pemilihan parameter yang tidak tepat. Kami menggunakan citra pemantauan IR tambang batubara untuk melihat kemampuan pengenalan target IR oleh SVM. Fitur dan kategori citra pemantauan IR tambang batubara diberikan. Hasil eksperimen menggambarkan bahwa akurasi pengenalan target IR oleh SVM adalah 100%. Jadi, SVM adalah metode pengenalan target IR yang sangat baik.
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References
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[4] Hsiu-Yuan Hu, Yu-Cheng Lee, Tieh-Min Yen, Chih-Hung Tsai, “Using BPNN and DEMATEL to modify importance–performance analysis model – A study of the computer industry”, Expert Systems with Applications, 2009, vol.36,no.6,pp.9969-9979.
[5] Wen-Chin Chen, Yung-Yuan Hsu, Ling-Feng Hsieh, Pei-Hao Tai, “A systematic optimization approach for assembly sequence planning using Taguchi method, DOE, and BPNN”, Expert Systems with Applications, 2010, vol.37,no.1,pp.716-726.
[6] J.D.B. Nelson, R.I. Damper, S.R. Gunn, B. Guo, “Signal theory for SVM kernel design with applications to parameter estimation and sequence kernels”,Neurocomputing, 2008,vol.72,no.1-3, pp.15-22.
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Published
2015-05-07
How to Cite
Amalia, R., Kuzairi, K., Yulianto, T., Mardianto, M. F. F., & Faisol, F. (2015). Aplikasi SVM Classifier dalam Pengenalan Target IR (infrared). Zeta - Math Journal, 1(1), 6–9. https://doi.org/10.31102/zeta.2015.1.1.6-9
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