Identification of the Freshness Level of Tuna based on Discrete Cosine Transform on Feature Extraction of Gray Level Co-Occurrence Matrix using K-Nearest Neighbor


Zulfrianto Yusrin Lamasigi(1*); Serwin Serwin(2); Yusrianto Malago(3);

(1) Universitas Ichsan Gorontalo
(2) Universitas Ichsan Gorontalo
(3) Universitas Ichsan Gorontalo
(*) Corresponding Author

  

Abstract


Gorontalo Province is one of the provinces that have fishery potential and has a large sea area that can be managed to support the economy and development of the province. Gorontalo is also one of the tuna-producing provinces in Indonesia, where tuna is also one of the mainstay fisheries commodities.  This study aimed to combine transformation and texture feature extraction methods to improve the identification of the freshness level of tuna. This research used Discrete Cosine Transform as transformation detection and Gray Level Co-Occurrence Matrix as texture feature extraction. To find out the value of the proximity of the training data and image testing of tuna fish, the K-Nearest Neighbor classification method was employed. Then, the Confusion Matrix was used to calculate the accuracy level of the K-Nearest Neighbor classification.   This research was carried out with 4 stages of testing, namely at angles of 0°, 45°, 90°, and 135°, and using the values of k=1, 3, 5, and 7. The test results of using training data of 428 images and testing data of 161 images in four classes used with angles of 0°, 45°, 90°, 135°, and the value of k=1, 3, 5, 7. The highest accuracy results was obtained at an angle of 0° with a value of k = 1 of 94.40%, while the lowest accuracy value was at an angle of 90° and 135° with a value of k=7 of 59%. This showed that the Discrete Cosine Transform transformation method was very effective to improve the performance of texture feature extraction of Gray Level Co-Occurrence Matrix in extracting tuna image features. It was proven from the results of the accuracy of the K-Nearest Neighbor classification obtained.


Keywords


Identification; Classification; DCT; GLCM; KNN; Confusion Matrix

  
  

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doi  https://doi.org/10.33096/ilkom.v15i1.1426.153-164 		  

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References


P. Gorontalo, “Pemerintah Provinsi Gorontalo, “Rencana Strategis (Renstra) Dinas Kelautan dan perikanan provinsi gorontalo,” https://dinaskp.gorontaloprov.go.id., 2017. https://dinaskp.gorontaloprov.go.id.

M. R. Gobel, M. Baruwadi, and A. Rauf, “Analisis Daya Saing Ikan Tuna Di Provinsi Gorontalo,” Jambura Agribus. J., vol. 1, no. 1, pp. 36–42, 2019, doi: 10.37046/jaj.v1i1.2448.

Z. Y. Lamasigi, “DCT Untuk Ekstraksi Fitur Berbasis GLCM Pada Identifikasi Batik Menggunakan K-NN,” Jambura J. Electr. Electron. Eng., vol. 3, no. 1, pp. 1–6, 2021, doi: 10.37905/jjeee.v3i1.7113.

Z. Y. Lamasgi, “Identifikasi Tingkat Kesegaran Ikan Tuna Menggunakan Metode GLCM dan KNN,” Jambura J. Electr. Electron. Eng., vol. 4, pp. 70–76, 2022.

C. Jatmoko and D. Sinaga, “Ektraksi Fitur Glcm Pada K-Nn Dalam Mengklasifikasi Motif Batik,” Pros. SENDI_U 2019, pp. 978–979, 2019.

M. Sholihin, “Identifikasi Kesegaran Ikan Berdasarkan Citra Insang dengan Metode Convolution Neural Network,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 8, no. 3, pp. 1352–1360, 2021, doi: 10.35957/jatisi.v8i3.939.

F. Astutik, “Sistem Pengenalan Kualitas Ikan Gurame Dengan Wavelet, Pca, Histogram Hsv Dan Knn,” Lontar Komput., vol. 4, no. 3, pp. 336–346, 2015, doi: 10.24843/LKJITI.

M. Begum, J. Ferdush, and M. S. Uddin, “A Hybrid robust watermarking system based on discrete cosine transform, discrete wavelet transform, and singular value decomposition,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 5856–5867, 2022, doi: 10.1016/j.jksuci.2021.07.012.

Z. Y. Lamasigi and A. Bode, “Influence of gray level co-occurrence matrix for texture feature extraction on identification of batik motifs using k-nearest neighbor,” Ilk. J. Ilmiah; Vol 13, No 3, Dec. 2021, [Online]. Available: http://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1025.

R. A. Surya, A. Fadlil, and A. Yudhana, “Ekstraksi Ciri Citra Batik Berdasarkan Tekstur Menggunakan Metode Gray Level Co Occurrence Matrix,” Prosiding, 6 Desember 2016, Vol 2 No. 1, vol. 2, no. 1, pp. 146–150, 2016.

Y. Fernando, “Klasifikasi Jenis Daging Berdasarkan Analisis Citra Tekstur Gray Level Co-Occurrence Matrices ( Glcm ) Dan Warna,” Semin. Nas. Sains dan Teknol. 2017, no. Fakultas Teknik Universitas Muhammadiyah Jakarta, pp. 1–7, 2017.

C. Irawan and E. H. Rachmawanto, “EKTRAKSI HSV DAN GLCM DALAM METODE K-NN UNTUK KLASIFIKASI TINGKAT KEMATANGAN BUAH MENGKUDU,” 2022, no. November, pp. 16–25.

I. Amalia, “Ekstraksi Fitur Citra Songket Berdasarkan Tekstur Menggunakan Metode Gray Level Co-occurrence Matrix (GLCM),” J. Infomedia, vol. 3, no. 2, pp. 64–68, 2018, doi: 10.30811/jim.v3i2.715.

Z. Y. Lamasigi, M. Hasan, and Y. Lasena, “Local Binary Pattern untuk Pengenalan Jenis Daun Tanaman Obat menggunakan K-Nearest Neighbor,” Ilk. J. Ilm., vol. 12, no. 3, pp. 208–218, 2020, doi: 10.33096/ilkom.v12i3.667.208-218.

M. I. Sultoni, B. Hidayat, A. S. Subandrio, T. Elektro, and U. Telkom, “Klasifikasi Jenis Batuan Beku Melalui Citra Berwarna Dengan Menggunakan Metode Local Binarypattern Dan K-Nearest Neighbor,” Geol. Sains, vol. 4, no. 1, pp. 10–15, 2019.

T. D. Novianto and I. M. S. Erawan, “Perbandingan Metode Klasifikasi pada Pengolahan Citra Mata Ikan Tuna,” Pros. SNFA (Seminar Nas. Fis. dan Apl., vol. 5, pp. 216–223, 2020, doi: 10.20961/prosidingsnfa.v5i0.46615.


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