SUPPORT VECTOR MACHINE BERBASIS ZERNIKE MOMENTS DALAM KLASIKASI CITRA BAKTERI
Abstract
Kontaminasi bakteri patogen dalam produk makanan mahal bagi masyarakat dan industri. Metode tradisional untuk mendeteksi dan mengidentifikasi patogen bawaan makanan seperti Listeria monocytogenes biasanya memakan waktu 3-7 hari. Pada makalah ini, sistem klasifikasi dikembangkan untuk mengidentifikasi pengambilan citra bakteri menggunakan alat optical light scattering dan menghasilkan citra berbentuk grayscale. Algoritma klasifikasi yang diusulkan didasarkan pada Invariant Zernike Moment berbasis Support Vector Machine pada kernel Radial Chebyshev Moments yang dihitung dari dataset citra bakteri apda 4 genus yang digunakan sebagai dataset. Sebanyak 400 citra bakteri dengan 100 citra pada masing-masing jenis genus telah di uji dan menghasilkan akurasi pada proses indetifikasi dengan capaian sebesar 78,33% pada 5-fold Cross Validation.
References
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[5] I. Gogul and V. S. Kumar, “Flower species recognition system using convolution neural networks and transfer learning,” 2017 4th Int. Conf. Signal Process. Commun. Networking, ICSCN 2017, pp. 1–6, 2017.
[6] A. Khotanzad and Y. H. Hong, “Invariant Image Recognition by Zernike Moments,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 12, no. 5, pp. 489–497, 1990.
[7] M. A. Chandra and S. S. Bedi, “Survey on SVM and their application in image classification,” Int. J. Inf. Technol., Jan. 2018.
[8] J. Kim, B.-S. Kim, and S. Savarese, “Comparing Image Classification Methods: K-Nearest-Neighbor and Support-Vector-Machines,” Appl. Math. Electr. Comput. Eng., pp. 133–138, 2012.
[9] G. M. Foody and A. Mathur, “A relative evaluation of multiclass image classification by support vector machines,” IEEE Trans. Geosci. Remote Sens., vol. 42, no. 6, pp. 1335–1343, Jun. 2004.
[10] C. E. Cabrera Ardila, L. Alberto Ramirez, and F. A. Prieto Ortiz, “Spectral analysis for the early detection of anthracnose in fruits of Sugar Mango (Mangifera indica),” Comput. Electron. Agric., vol. 173, no. March, p. 105357, Jun. 2020.
[11] Y. Chen et al., “Variety identification of orchids using Fourier transform infrared spectroscopy combined with stacked sparse auto-encoder,” Molecules, vol. 24, no. 13, 2019.
[12] S. K. Mishra and V. H. Deepthi, “Brain image classification by the combination of different wavelet transforms and support vector machine classification,” J. Ambient Intell. Humaniz. Comput., no. 0123456789, Jul. 2020.
[13] S. Zhang, X. Wu, S. Zhang, Q. Cheng, and Z. Tan, “An effective method to inspect and classify the bruising degree of apples based on the optical properties,” Postharvest Biol. Technol., vol. 127, pp. 44–52, 2017.
[14] P. B. Padol and A. A. Yadav, “SVM classifier based grape leaf disease detection,” Conf. Adv. Signal Process. CASP 2016, pp. 175–179, 2016.
[15] Y.-L. Bei, S. Qiao, M.-X. Liu, X.-R. Zhu, and Q. Zhang, “A Color Image Watermarking Scheme Against Geometric Rotation Attacks Based on HVS and DCT-DWT,” in 2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), 2018, pp. 343–347.
Published
2021-09-30
How to Cite
Kusumawati, Y., & Mulyono, I. (2021). SUPPORT VECTOR MACHINE BERBASIS ZERNIKE MOMENTS DALAM KLASIKASI CITRA BAKTERI. Dinamik, 26(2), 80-97. https://doi.org/10.35315/dinamik.v26i2.8674
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