Classification of Indonesian Kitchen Spices Using the K-Nearest Neighbors (K-NN) Method
##plugins.themes.bootstrap3.article.sidebar##
##plugins.themes.bootstrap3.article.main##
Abstract
Seasoning is one of the most important elements in a dish. Indonesian herbs or spices have a very wide variety of types. Mistakes in choosing spices have a big effect on the taste of the dish. Image processing is a branch of science in the field of technology that can be used to recognize image objeks captured by the camera. This study will classify the types of spices that are almost similar, namely ginger, galangal, turmeric and kencur. The classification method used is K-Nearest Neighbor (K-NN). In this study we tested how to split training data and data testing, namely 66.7%: 33.33%, 75%: 25% and 90%: 10%. The sharing of training data and testing data uses 90%: 10% has the greatest average accuracy compared to other distribution methods. The selection of K = 3 or K = 5 has an average accuracy that is almost the same in all methods of split training data and testing data, namely 64.66%: 65%. At K = 1 it has a fairly high accuracy compared to the previous K, which is 73%.
##plugins.themes.bootstrap3.article.details##
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
The writer agreed that the article copyright by Smatika journal and the writer has the right to disseminate the paper published without permission in advance.
[2]S. M. Ayyad, A. I. Saleh, and L. M. Labib, “Gene expression cancer classification using modified K-Nearest Neighbors technique,” BioSystems, vol. 176, no. December 2018, pp. 41–51, 2019, doi: 10.1016/j.biosystems.2018.12.009.
[3]A. R. Widiarti, “K-nearest neighbor performance for Nusantara scripts image transliteration,” J. Teknol. dan Sist. Komput., vol. 8, no. 2, pp. 150–156, 2020, doi: 10.14710/jtsiskom.8.2.2020.150-156.
[4]S. H. Wardani, T. Rismawan, and S. Bahri, “Aplikasi Klasifikasi Jenis Tumbuhan Mangrove Berdasarkan Karakteristik Morfologi Menggunakan Metode K-Nearest Neighbor (KNN) Berbasis Web,” Coding J. Komput. dan Apl. Untan, vol. 04 (3), no. 3, pp. 9–21, 2016.
[5]E. Budianita, J. Jasril, and L. Handayani, “Implementasi Pengolahan Citra dan Klasifikasi K-Nearest Neighbour Untuk Membangun Aplikasi Pembeda Daging Sapi dan Babi Berbasis Web,” J. Sains dan Teknol. Ind., vol. 12, no. Vol 12, No 2 (2015): Juni 2015, pp. 242–247, 2015, [Online]. Available: http://ejournal.uin-suska.ac.id/index.php/sitekin/article/view/1005.
[6]B. S. Hutagaol, Y. A. Sari, and P. P. Adikara, “Ekstraksi Fitur RGB Color Channel dan Simple Morphological Shape Descriptors dari Citra Makanan untuk Pencarian Resep Makanan,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 3, pp. 2923–2928, 2019.
[7]R. Arian, A. Hariri, A. Mehridehnavi, A. Fassihi, and F. Ghasemi, “Protein kinase inhibitors’ classification using K-Nearest neighbor algorithm,” Comput. Biol. Chem., vol. 86, p. 107269, 2020, doi: 10.1016/j.compbiolchem.2020.107269.
[8]A. Rosebrock, Deep Learning for Computer Vision With Python, 3rd ed. United States of America: PyImageSearch, 2018.
[9] O. S. Y. Prakasa and K. M. Lhaksmana, “Klasifikasi Teks Dengan Menggunakan Algoritma K-nearest Neighbor Pada Kasus Kinerja Pemerintah Di Twitter,” eProceedings Eng., vol. 5, no. 3, pp. 8237–8248, 2018.
[10]L. Farokhah, “Implementasi K-Nearest Neighbor Untuk Klasifikasi Bunga Implementation of K-Nearest Neighbor for Flower Classification With Extraction of Rgb Color Features,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 6, pp. 1129–1136, 2020, doi: 10.25126/jtiik.202072608.