Citra Digital Untuk Klasifikasi Kualitas Udang Windu Menggunakan Algoritma GLCM dan K-Nearest Neighbor

Najirah Umar, Fiqri Haikal, M. Razak
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Abstract

Shrimp is a food that is easily damaged based on direct observation of the shrimp sorting process carried out by distributors or fishermen to select shrimp based on quality still using the manual method and sometimes the sorting results are still not in accordance with the quality of the shrimp and the quality indicators are only seen from the physical such as the weight or size of the shrimp, so that good quality shrimp can be mixed with less good quality, therefore contamination will occur which causes good quality shrimp to rot quickly. This final project aims to build an image processing system that applies the Gray-level Co-occurrence Matrix (GLCM) and K-nearest Neighbor (K-NN) algorithms to detect the quality level of Windu shrimp. The first process in this research is to perform image acquisition. That is, collecting several digital images of each quality of shrimp to use as an object. In addition, a pre-processing process is also carried out, namely changing the image to grayscale. Then the feature extraction process uses the Gray Level Co-occurrence Matrix (GLCM) method to obtain feature data from all digital images and classify them using the K-Nearest Neighbor (K-NN) method. The test results give an accuracy of 10 samples, it was found that as much as 80% got the results of quality classification information in accordance with the system. And this system is able to provide decision solutions in determining the quality classification of Windu Shrimp, while based on the results of blackbox testing, this system produces a percentage of application ease of use as much as 92%.
Windu对虾质量分类的数字图像使用GLCM算法和K-Nearest算法
虾是一种食物,很容易受损的基础上直接观察虾的排序过程由分销商或虾渔民选择基于质量仍然使用手工方法排序结果,有时还没有根据虾的质量和质量等物理指标只看到虾的重量或大小,以便质量好的虾混合质量不太好的,因此,污染会发生,导致优质虾迅速腐烂。本次期末项目的目标是构建一个图像处理系统,应用灰度共生矩阵(GLCM)和k -近邻(K-NN)算法检测Windu虾的质量水平。本研究的第一个过程是进行图像采集。也就是说,收集虾的每一种品质的几张数字图像作为一个对象。此外,还进行了预处理处理,即将图像变为灰度。然后,特征提取过程使用灰度共生矩阵(GLCM)方法从所有数字图像中获取特征数据,并使用k -最近邻(K-NN)方法对其进行分类。测试结果给出了10个样品的准确率,结果发现,高达80%的质量分类信息与系统得到的结果相符。该系统能够为Windu对虾的品质分类提供决策解决方案,基于黑盒测试结果,该系统的应用易用性百分比高达92%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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24 weeks
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