Implementasi HSV dan GLCM untuk Deteksi Kesegaran Ikan Bawal menggunakan Radial Basis Function Berbasis Android

  • Muhammad Sarimin Universitas Maritim Raja Ali Haji
  • Nurul Hayaty Universitas Maritim Raja Ali Haji
  • Martaleli Bettiza Universitas Maritim Raja Ali Haji
  • Sapta Nugraha Universitas Maritim Raja Ali Haji
Keywords: HSV, GLCM, RBF, K-Means

Abstract

Tanjungpinang is one of the fish producing cities. fish with a good level of freshness are needed to produce quality fish products. In this case, a system is needed that can recognize fresh and non-fresh fish. In this study using the HSV and GLCM methods as a feature then image recognition is carried out using the Radial Basis Function (RBF). In the RBF recognition method it is necessary to have a central point that becomes the data center. Data center retrieval uses the K-Means method, where this method greatly determines the success of the RBF's introduction. By determining the best number of data centers in the best data center, it is at number 7 with MAD of 0.98. At the time of image acquisition did not pay attention to lighting so as to produce training data with low quality. How in the introduction process using this RBF gets a low level of accuracy, which is equal to 50%

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Published
2019-05-31
How to Cite
[1]
M. Sarimin, N. Hayaty, M. Bettiza, and S. Nugraha, “Implementasi HSV dan GLCM untuk Deteksi Kesegaran Ikan Bawal menggunakan Radial Basis Function Berbasis Android”, sustainable, vol. 8, no. 1, pp. 1-7, May 2019.