KLASIFIKASI BUNGA MAWAR MENGGUNAKAN KNN DAN EKSTRAKSI FITUR GLCM DAN HSV
Keywords:
Mawar, Klasifikasi, GLCM, HSV, K-NN
Abstract
Bunga mawar biasanya di produksi sebagai bahan kecantikan maupun parfum. Bunga mawar dapat dibudidayakan untuk bunga potong dan bunga hias. Peneltian ini bertujuan untuk mengklasifikasikan bunga mawar kedalam 5 kategori yaitu bunga mawar doube delight, bunga mawar megawati, bunga awar musk, bunga mawar putri dan bunga mawar thalita yang di dominasi dengan warna merah. Diketahui beberapa jenis mawar mempunyai tampilan kelopak bunga maupun warna yang sama. Kesamaan bentuk dan warna membuat proses klasifikasi berdasarkan mata manusia saja menjadi lebih sulit, sehingga membutuhkan teknik pengolahan citra. Beberapa penelitian bunga mawar hanya menggunakan satu ekstraksi fitur saja sehingga kurang akurat. Dalam penelitian ini telah digunakan algoritma KNN dan ekstraksi fitur GLCM-HSV. Nilai fitur yang digunakan berupa ekstraksi tekstur GLCM dan warna HSV yang nantinya akan dijadikan sebagai parameter perhitungan klasifikasi menggunakan K-NN berdasarkan Euclidean Distance. Data yang digunakan sebanyak 100 data latih dan 25 data uji. Hasil akurasi klasifikasi tertinggi terletak pada K=3 yaitu 96%.
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[5] P. N. Andono, E. H. Rachmawanto, N. S. Herman, and K. Kondo, “Orchid types classification using supervised learning algorithm based on feature and color extraction,” Bull. Electr. Eng. Informatics, vol. 10, no. 5, pp. 2530–2538, 2021.
[6] F. Wibowo, D. K. Hakim, and S. Sugiyanto, “Pendugaan Kelas Mutu Buah Pepaya Berdasarkan Ciri Tekstur Glcm Menggunakan Algoritma K-Nearest Neighbors,” Jurnal Nasional Pendidikan Teknologi Informasi, vol. 7, no. 1, p. 100, 2018.
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[8] A. Peryanto, A. Yudhana, and R. Umar, “Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation,” Journal of Applied Informatics and Computing, vol. 4, no. 1, pp. 45–51, 2020.
[9] N. Nafiah, “Klasifikasi Kematangan Buah Mangga Berdasarkan Citra HSV dengan KNN,” Jurnal Elektronik List. dan Teknologi Informasi Terapan, vol. 1, no. 2, pp. 1–4, 2019.
[10] M. Nasir, N. Suciati, and A. Y. Wijaya, “Kombinasi Fitur Tekstur Local Binary Pattern yang Invariant Terhadap Rotasi dengan Fitur Warna Berbasis Ruang Warna HSV untuk Temu Kembali Citra Kain Tradisional,” Inspir. Jurnal Teknologi Informasi dan Komunikasi, vol. 7, no. 1, 2017.
[11] N. Krithika and A. Grace Selvarani, “An individual grape leaf disease identification using leaf skeletons and KNN classification,” Proc. 2017 Int. Conf. Innov. Information, Embed. Commun. Syst. ICIIECS 2017, vol. 2018-Januari, pp. 1–5, 2018.
[12] A. E. Minarno, F. D. Setiawan Sumadi, H. Wibowo, and Y. Munarko, “Classification of batik patterns using K-Nearest neighbor and support vector machine,” Bull. Electr. Eng. Informatics, vol. 9, no. 3, pp. 1260–1267, 2020.
[13] T. Y. Prahudaya and A. Harjoko, “Metode Klasifikasi Mutu Jambu Biji Menggunakan Knn Berdasarkan Fitur Warna Dan Tekstur,” J. Teknosains, vol. 6, no. 2, p. 113, 2017.
[14] A. Akbar, B. Siswojo, and H. Suyono, “Klasifikasi Mutu Mutiara Berdasarkan Bentuk Dan Ukuran Menggunakan K-Nearest Neighbor,” CESS (Journal Comput. Eng. Syst. Sci., vol. Vol. 2, no. 2, p. 5, 2017.
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Published
2022-07-26
How to Cite
[1]
W. Sari and C. Sari, “KLASIFIKASI BUNGA MAWAR MENGGUNAKAN KNN DAN EKSTRAKSI FITUR GLCM DAN HSV”, SKANIKA, vol. 5, no. 2, pp. 145-156, Jul. 2022.
Section
Articles
