Fuzzy Learning Vector Quantization for Classification of Mixed Meat Image Based on Character of Color and Texture
Beef consumption is quite high and expensive in the world. In Indonesia, beef prices are relatively expensive because the meat supply chain from farmers to the market is quite long. The high demand for beef and the difficulty of obtaining meat are factors in the high price of meat. This makes some meat traders cheat by mixing beef and pork (oplosan). Mixing beef and pork is detrimental to beef consumers, especially those who are Muslim. In this paper, we proposed a new strategy for identifying beef, pig, and mixed meat utilizing Fuzzy learning vector quantization (FLVQ) Based on the color and texture aspects of the meat. The HSV (Hue saturation value) approach is used for color features, whereas the GLCM (Gray level co-occurrence matrix) method is used for texture features. This study makes use of primary data collected from the Pasar Bawah Tourism and Cipuan Market in Pekanbaru, Riau Province. The data set consists of 600 photos, 200 each of beef, pork, and mixed. Based on the test scenario, the coefficient of fuzzyness and learning rate affect the accuracy of meat image identification. The proposed strategy has succeeded in classifying pork, beef and mixed meat with the best percentage of accuracy results in theclasses of beef and pork, beef and mixed, pork and mixed meat, respectively, at 100%, 97.5%, and 95%. This demonstrates that the proposed strategy has succeeded in classifying the image of pork, beef, and mixed.
L. Arsilo and K. Ma’mur, “Implementasi Sistem Klasifikasi Analisa Tekstur Dan Normalisasi Warna Terhadap Daging Sapi Dan Daging Babi,” vol. 1, no. 03, pp. 216–224, 2022.
N. A. Nafiasari and A. M. Handayani, “Penganalisis kesegaran daging sapi dan daging babi mentah berdasarkan klasifikasi warna dan kelembaban,” vol. 6131, pp. 66–74, 2018, doi: 10.22146/teknosains.35643.
U. Sudibyo, D. P. Kusumaningrum, E. H. Rachmawanto, and C. A. Sari, “Optimasi Algoritma Learning Vector Quantization (Lvq) Dalam Pengklasifikasian Citra Daging Sapi Dan Daging Babi Berbasis GLCM Dan HSV,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 9, no. 1, pp. 1–10, 2018, doi: 10.24176/simet.v9i1.1943.
R. A. Asmara et al., “Classification of pork and beef meat images using extraction of color and texture feature by Grey Level Co-Occurrence Matrix method,” IOP Conf. Ser. Mater. Sci. Eng., vol. 434, no. 1, p. 012072, Nov. 2018, doi: 10.1088/1757-899X/434/1/012072.
Jasril and S. Sanjaya, “Learning Vector Quantization 3 ( LVQ3 ) and Spatial Fuzzy C- Means ( SFCM ) for Beef and Pork Image Classification,” vol. 1, no. 2, pp. 60–65, 2018.
L. Handayani, “Analisa Metode Gabor dan Propbabilistic Neural Network untuk Klasifikasi Citra ( Studi Kasus : Citra Daging Sapi dan Babi ),” vol. 14, no. 2, pp. 169–177, 2017.
D. R. Wijaya, R. Sarno, and A. F. Daiva, “Electronic nose for classifying beef and pork using Naïve Bayes,” Proc. - 2017 Int. Semin. Sensor, Instrumentation, Meas. Metrol. Innov. Adv. Compet. Nation, ISSIMM 2017, vol. 2017-January, pp. 104–108, Nov. 2017, doi: 10.1109/ISSIMM.2017.8124272.
L. Bing and W. Wang, “Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification,” Comput. Math. Methods Med., vol. 2017, 2017, doi: 10.1155/2017/7894705.
D. Wandi, Fauziah, and N. Hayati, “Deteksi kelayuan bunga mawar dengan metode transformasi ruang warna hsi dan hsv,” vol. 5, no. 3, pp. 333–341, 2021.
Ş. Öztürk and B. Akdemir, “Application of Feature Extraction and Classification Methods for Histopathological Image using GLCM , LBP, LBGLCM, GLRLM, and SFTA,” Procedia Comput. Sci., vol. 132, no. Iccids, pp. 40–46, 2018, doi: 10.1016/j.procs.2018.05.057.
H. Himawan and W. Wiratama, “Different Types of Beef And Pork Using Histogram Texture Anda K-Means Clustering Method,” vol. 3, no. 1, pp. 20–27, 2018.
F. Zhang et al., “Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction,” IEEE Access, vol. 8, pp. 104555–104564, 2020, doi: 10.1109/ACCESS.2020.2999608.
J. M. Chaves-González, M. A. Vega-Rodríguez, J. A. Gómez-Pulido, and J. M. Sánchez-Pérez, “Detecting skin in face recognition systems: A colour spaces study,” Digit. Signal Process., vol. 20, no. 3, pp. 806–823, May 2010, doi: 10.1016/J.DSP.2009.10.008.
Y. Wan and Q. Chen, “Joint image dehazing and contrast enhancement using the HSV color space,” 2015 Vis. Commun. Image Process. VCIP 2015, vol. 1, no. 3, pp. 2–5, 2015, doi: 10.1109/VCIP.2015.7457892.
E. Budianita, Jasril, and L. Handayani, “Implementasi Pengolahan Citra dan Klasifikasi K-Nearest Neighbour Untuk Membangun Aplikasi Pembeda Daging Sapi dan Babi Berbasis Web,” J. Sains dan Teknol. Ind., vol. 12, no. Vol 12, No 2 (2015): Juni 2015, pp. 242–247, 2015, [Online]. Available: http://ejournal.uin-suska.ac.id/index.php/sitekin/article/view/1005.
M. Garg and G. Dhiman, “A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants,” Neural Comput. Appl. 2020 334, vol. 33, no. 4, pp. 1311–1328, Jun. 2020, doi: 10.1007/S00521-020-05017-Z.
A. Ali, X. Jing, and N. Saleem, “GLCM-based fingerprint recognition algorithm,” in Proceedings - 2011 4th IEEE International Conference on Broadband Network and Multimedia Technology, IC-BNMT 2011, 2011, pp. 207–211, doi: 10.1109/ICBNMT.2011.6155926.
P. K. Mall, P. K. Singh, and D. Yadav, “GLCM based feature extraction and medical X-RAY image classification using machine learning techniques,” 2019 IEEE Conf. Inf. Commun. Technol. CICT 2019, pp. 1–6, 2019, doi: 10.1109/CICT48419.2019.9066263.
S. A. Wibowo, B. Hidayat, and U. Sunarya, “Simulasi dan Analisis Pengenalan Citra Daging Sapi dan Daging Babi dengan Metode GLCM,” pp. 338–343, 2016.
A. Damayanti, “Fuzzy learning vector quantization, neural network and fuzzy systems for classification fundus eye images with wavelet transformation,” Proc. - 2017 2nd Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. ICITISEE 2017, vol. 2018-January, pp. 331–336, Feb. 2018, doi: 10.1109/ICITISEE.2017.8285522.
P. Utomo, Wiharto, and E. Suryani, “Sistem Diagnosa Penyakit Paru Berdasarkan Foto Rontgen Dengan Pendekatan Fuzzy Learning Vector Quantization,” ITSMART J. Teknol. dan Inf., vol. 1, no. 2, pp. 102–106, Mar. 2016, doi: 10.20961/ITSMART.V1I2.604.
W. Jatmiko et al., “Development of Adaptive Fuzzy-Neuro Generalized Learning-Vector Quantization Using PI Membership Function (AFNGLVQ-PI),” IEEEAccess, 2021. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9343316 (accessed Jul. 14, 2021).
M. Rabin and M. Rahwi, “Klasifikasi Kualitas Susu Segar Menggunakan Metode Fuzzy Learning Vector Quantization,” 2018.
F. Syafria, A. Buono, and P. Silalahi, “Pengenalan Suara Paru-Paru dengan MFCC sebagai Ekstraksi Ciri dan Backpropagation sebagai Classifier Lung Sound Recognition using MFCC as A Feature Extraction and Backpropagation as A Classifier,” 2014, Accessed: Jun. 28, 2021. [Online]. Available: http://journal.ipb.ac.id/index.php/jika.
W. Jatmiko, Rochmatullah, B. Kusumoputro, K. Sekiyama, and T. Fukuda, “Fuzzy learning vector quantization based on particle swarm optimization for artificial odor dicrimination system,” WSEAS Trans. Syst., vol. 8, no. 12, pp. 1239–1252, 2009.
M. F. Rachmadi, M. A. Ma’sum, I. M. A. Setiawan, and W. Jatmiko, “Fuzzy Learning Vector Quantization Particle Swarm Optimization ( FLVQ-PSO ) and Fuzzy Neuro Generalized Learning Vector Quantization ( FN-GLVQ ) for Automatic Early Detection System of Heart Diseases based on Real-time Electrocardiogram,” pp. 465–470, 2012.
Copyright (c) 2022 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;