Identifikasi citra daging ayam berformalin menggunakan metode fitur tekstur dan k-nearest neighbor (K-NN)

Amin, Faris Mushlihul (2018) Identifikasi citra daging ayam berformalin menggunakan metode fitur tekstur dan k-nearest neighbor (K-NN). Jurnal Matematika “MANTIK, 4 (1). pp. 68-74. ISSN 2527-3159 : 2527-3167

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Faris Mushlihul Amin_Identifikasi citra daging ayam berformalin menggunakan metode fitur tekstur dan k-nearest neighbor.pdf
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Abstract

The research aimed to create a fresh chicken meat identification system to detect differences between formalin and non-formalin chicken meat based on the image of raw chicken meat. Feature extraction method used is the Feature Texture method which is included in the statistical method where the statistical calculation uses a gray degree distribution (histogram) by measuring the level of contrast, granularity, and roughness of an area from the neighboring relationships between pixels in the image then feature extraction, results feature extraction is then classified by K-NN. With the classification using K-NN results obtained high classification accuracy. The K-NN method is a very good method of dealing with the problem of recognizing complex patterns in the form of data training and processing calibration, based on very fast and high accurate literature methods more than other methods. Observation images will be carried out at various distances between the smartphone camera and chicken meat samples.

Item Type: Article
Creators:
Creators
Email
["eprint_fieldname_creators_NIDN" not defined]
Amin, Faris Mushlihul
faris@uinsby.ac.id
2013088801
Uncontrolled Keywords: Chicken; Texture Feature; K-NN
Subjects: 01 MATHEMATICAL SCIENCES > 0101 Pure Mathematics > 010107 Mathematical Logic, Set Theory, Lattices and Universal Algebra
01 MATHEMATICAL SCIENCES > 0102 Applied Mathematics > 010201 Approximation Theory and Asymptotic Methods
Divisions: Karya Ilmiah > Artikel
Depositing User: Aris Hamidah
Date Deposited: 30 Jul 2021 04:55
Last Modified: 30 Jul 2021 04:55
URI: http://repository.uinsa.ac.id/id/eprint/542

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