Klasifikasi Kesegaran Daging Sapi Menggunakan Metode Ekstraksi Tekstur GLCM dan KNN Freshness Classification of Beef Using GLCM Texture Extraction Method and KNN

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Ade Prabowo
Danang Erwanto
Putri Nur Rahayu

Abstract

Meat is the soft part of the animal that is covered by skin and is attached to the bones which become food ingredients. This research was conducted to classify the types of fresh, inn and rotten beef using 120 samples of beef taken directly by the researcher. Before classifying the type of beef, the texture of the beef image was extracted using the GLCM method to produce texture parameters in the form of contrast, correlation, homogeneity and energy. Texture parameters are classified using the KNN method. The results in this study indicate that the extraction of beef image texture using the GLCM method can produce various values on the 4 parameters of the GLCM texture. In addition, the results of the classification of beef freshness using the KNN method to determine 3 types of meat quality, namely fresh, cooked and rotten beef, obtained an evaluation of the classification performance using the Confusion Matrix table with an Accuracy value of 0.82, Precision of 0.83, Recall of 0.82 and F-Measure of 0.82. So that the parameters of the beef image texture using the GLCM method can be classified properly using the KNN method.

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Prabowo, A., Erwanto, D., & Nur Rahayu, P. (2021). Klasifikasi Kesegaran Daging Sapi Menggunakan Metode Ekstraksi Tekstur GLCM dan KNN. Electro Luceat, 7(1), 74-81. https://doi.org/10.32531/jelekn.v7i1.344
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References

[1] A. Azem, M. Ulum, dan K. A. Wibisono, “Rancang Bangun Alat Deteksi Kesegaran Daging Berdasarkan Ciri Warna dan Bau Menggunakan Metode Fuzzy Sugeno,” SinarFe7, vol. 2, no. 1, hal. 287–291, 2019.

[2] L. Cahyono, “IDENTIFIKASI DAGING SAPI SEGAR DAN BEKU MENGGUNAKAN NEURAL NETWORK,” Universitas Mercu Buana Yogyakarta, 2019.

[3] F. Rosyad dan D. Lenono, “Klasifikasi kemurnian daging sapi berbasis electronic nose dengan metode principal component analysis,” IJEIS (Indonesian J. Electron. Instrum. Syst, vol. 6, no. 1, hal. 47, 2016.

[4] H. Yunita dan E. Setyati, “Hand Gesture Recognition Sebagai Pengganti Mouse Komputer Menggunakan Kamera,” J. ELTIKOM J. Tek. Elektro, Teknol. Inf. dan Komput., vol. 3, no. 2, hal. 64–76, 2019.

[5] B. O. Hua, M. A. Fu-Long, dan J. Li-Cheng, “Research on computation of GLCM of image texture,” Acta Electron. Sin., vol. 1, no. 1, hal. 155–158, 2006.

[6] D. Rahmawati, M. P. Putri, M. Ulum, K. Joni, dan others, “Identification and Classification of Pathogenic Bacteria Using the K-Nearest Neighbor Method,” JEEE-U (Journal Electr. Electron. Eng., vol. 5, no. 1, hal. 60–70, 2021.

[7] A. Budianto, R. Ariyuana, dan D. Maryono, “Perbandingan K-Nearest Neighbor (KNN) Dan Support Vector Machine (SVM) Dalam Pengenalan Karakter Plat Kendaraan Bermotor,” J. Ilm. Pendidik. Tek. dan Kejuru., vol. 11, no. 1, hal. 27–35, 2018.

[8] T. Wijaya, H. Ginardi, dan W. Khotimah, “Paduan Elemen Warna Sa* b* pada Analisa Urin Dipstick dari Citra Hasil Kamera Smartphone dengan Jaringan Backpropagation,” Lontar Komput. J. Ilm. Teknol. Inf., 2014.

[9] D. W. Wibowo, D. Erwanto, dan D. A. W. Kusumastutie, “Klasifikasi Jenis Kayu Menggunakan Esktrasi Fitur Gray Level Co-Occurence Matrix dan Multilayer Perceptron,” J. Nas. Tek. Elektro, vol. 10, no. 1, hal. 1–10, 2021.

[10] H. R. Fajrin, H. A. Nugroho, dan I. Soesanti, “Ekstraksi Ciri Berbasis Wavelet Dan Glcm Untuk Deteksi Dini Kanker Payudara Pada Citra Mammogram,” Pros. SNST Fak. Tek., vol. 1, no. 1, 2015.

[11] S. F. Kusuma, R. E. Pawening, dan R. Dijaya, “Otomatisasi klasifikasi kematangan buah mengkudu berdasarkan warna dan tekstur,” Regist. J. Ilm. Teknol. Sist. Inf., vol. 3, no. 1, hal. 17–23, 2017.

[12] T. Y. Prahudaya dan A. Harjoko, “Metode Klasifikasi Mutu Jambu Biji Menggunakan Knn Berdasarkan Fitur Warna Dan Tekstur,” J. Teknosains, vol. 6, no. 2, hal. 113–123, 2017.

[13] M. M. Baharuddin, H. Azis, dan T. Hasanuddin, “Analisis Performa Metode K-Nearest Neighbor untuk Identifikasi Jenis Kaca,” Ilk. J. Ilm., vol. 11, no. 3, hal. 269–274, 2019.

[14] E. Prasetyo, “Data mining mengolah data menjadi informasi menggunakan matlab,” 2019.
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