Identification of Buni Fruit Image Using Euclidean Distance Method
DOI:
10.33395/sinkron.v7i2.11333Keywords:
Identification, Euclidean Distance, Texture, Color, Buni Fruit ImageAbstract
Identification is an important part of image analysis because in this procedure the desired image/image will be analyzed for further processing to make it easier to analyze for further purposes, for example in image identification pattern recognition which is part of image analysis used to divide an image into several parts and take some of the desired objects. This study aims to identify buni fruit with Euclidean distance and extract shape and texture features. Extraction of shape features using boundary metrics and whimsy. This boundary is considered to be able to recognize objects based on their shape and can distinguish them from other objects. For identification expositions, Euclidean distance is used which serves to represent the level of similarity of two images that take into account the distance value of the Euclidean distance. From the results of the evaluation using a disarray network by calculating precision, review, and accuracy, in order to identify the image of the buni fruit object properly.
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References
C. N. Prabiantissa, A. R. Tri, and R. A. Asmara, “Identification System for Natural Batik and Synthetic Batik Based on Image Color Characteristics Using K-Means Clustering Method,” J. Inform. Polynema, vol. 3, no. 2, p. 26, 2017, doi:10.33795/jip.v3i2.10.
D. I. Mulyana, "Optimization of Image Classification Using the Convolutional Neural Network (CNN) Algorithm for Cirebon Batik Image Indonesian," no. 12, pp. 39–46, 2021.
D. Nurnaningsih, D. Alamsyah, A. Herdiansah, and A. A. J. Sinlae, "Identification of Rhizome Types of Medicinal Plants Image by Euclidean Distance Based on Shape and Texture Characteristics," Build. Informatics, Technol. Sci., vol. 3, no. 3, pp. 171–178, 2021, doi:10.47065/bits.v3i3.1019.
H. Prabowo, "Detection of Ripeness Conditions of Citrus Fruits Based on Color Similarities in Android-Based RGB Color Space," J. Elektron. Sis. inf. and Computing., vol. 3, no. 2, pp. 9–19, 2017.
M. Qomaruddin, D. Riana, and A. Anton, “K-Means Segmentation of Tin Leaf Image with Gray Level Co Occurance Matrix Characteristic Classification,” J. Sist. and Technol. Inf., vol. 9, no. 2, p. 223, 2021, doi:10.26418/justin.v9i2.44139.
N. Sivi Anisa and T. Herdian Andika, "Segmentation-Based Leaf Image Identification System Using K-Means Clustering Method," Aisyah J. Informatics Electr. Eng., vol. 2, no. 1, pp. 9–17, 2020, doi:10.30604/jti.v2i1.22.
R. Enggar Pawening, W. Ja, and far Shudiq, “CLASSIFICATION OF THE QUALITY OF LOCAL ORANGE BASED ON TEXTURE AND SHAPE USING k-NEAREST NEIGHBOR (k-NN) METHOD,” Ejournal.Unuja.Ac.Id, vol. 1, no. 1, pp. 10–17, 2020.
S. Hadianti and D. Riana, "Segmentation of Bemisia Tabaci Image Using the K-Means Method," Semin. Nas. Inov. and Trends, p. 2018, 2018.
S. R. Raysyah, Veri Arinal, and Dadang Iskandar Mulyana, "Classification of Coffee Fruit Maturity Levels Based on Color Detection Using Knn and Pca Methods," JSiI (Jurnal System Information), vol. 8, no. 2, pp. 88–95, 2021, doi:10.30656/jsii.v8i2.3638.
S. Sukemi, “Sinta Sukemi,” 2021.
SN Saragih, M. Safii, and D. Suhendro, "Implementation of the K-Means Method on Livestock Meat Production," Jurassic (Jurnal Ris. Sist. Inf. and Tek. Inform., vol. 6, no. 1, p. 235, 2021, doi:10.30645/jurasik.v6i1.288.
Z. D. Lestari, N. Nafi'iyah, and P. H. Susilo, "Banana Type Classification System Based on HSV Color Characteristics Using the K-NN Method," Semin. Nas. technol. inf. and Commun., pp. 11–15, 2019.
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Copyright (c) 2022 Abdul Hafidz, Dadang Iskandar Mulyana, Dyan Bagus Sumantri, Kurniawan Setyo Nugroho
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