Implementasi Binarisasi Citra Menggunkan Metode K-Nearest Neighbor Untuk Mengindentifikasi Bawang Bombay Merah dan Bawang Bombay Putih

R Rahmad(1*), S Syafril(2), Agung Ramadhanu(3),

(1) Universitas Putra Indonesia “YPTK” Padang, Indonesia
(2) Universitas Putra Indonesia “YPTK” Padang, Indonesia
(3) Universitas Putra Indonesia “YPTK” Padang, Indonesia
(*) Corresponding Author

Abstract


The processing of digital image data is increasingly being developed and applied in various fields, one of which is object identification based on shape and color. This research aims to implement an image binarization method using the K-Nearest Neighbor (K-NN) method to identify two types of onions, namely red onions and white onions. The binarization method is used to convert color images into binary images, facilitating the feature extraction process. In this study, the features extracted from onion images include texture, shape, and color. K-NN is used as a classification algorithm to differentiate between the two types of onions based on these features. The results of the research indicate that the image binarization method and K-NN can identify red onions and white onions with a fairly high level of accuracy. The results of this implementation are expected to contribute to the development of an automatic recognition system for the classification of agricultural commodities.

Full Text:

PDF

References


A. Zalvadila, “Klasifikasi Penyakit Tanaman Bawang Merah Menggunakan Metode SVM dan CNN,” J. Inform. J. Pengemb. IT, vol. 8, no. 3, pp. 255–260, 2023, doi: 10.30591/jpit.v8i3.5341.

A. F. A. Jihad, F. Zulfa, and M. Bahar, “Uji efektivitas ekstrak bawang bombai (Allium Cepa L. Var. Cepa) terhadap pertumbuhan jamur mallasezia furfur secara in vitro,” Semin. Nas. Ris. Kedokt., vol. 1, no. 1, pp. 295–303, 2020.

M. K. Khamdani, N. Hidayat, and R. K. Dewi, “Implementasi Metode K-Nearest Neighbor Untuk Mendiagnosis Penyakit Tanaman Bawang Merah,” vol. 5, no. 1, pp. 11–16, 2021.

Y. Reswan, R. Toyib, H. Witriyono, and A. Anggraini, “Klasifikasi Tingkat Kematangan Buah Nanas Berdasarkan Fitur Warna Menggunakan Metode K-Nearest Neighbor (KNN),” J. Media Infotama, vol. 20, no. 1, pp. 280–287, 2024.

A. J. T, D. Yanosma, and K. Anggriani, “Implementasi Metode K-Nearest Neighbor (Knn) Dan Simple Additive Weighting (Saw) Dalam Pengambilan Keputusan Seleksi Penerimaan Anggota Paskibraka,” Pseudocode, vol. 3, no. 2, pp. 98–112, 2017, doi: 10.33369/pseudocode.3.2.98-112.

M. S. SIMANJUNTAK, “Identifikasi Tanda Tangan menggunakan Metode Fitur Ekstrasi Biner dan K Nearest Neighbor,” CSRID (Computer Sci. Res. Its Dev. Journal), vol. 12, no. 3, p. 191, 2021, doi: 10.22303/csrid.12.3.2020.191-200.

M. Kurniawan, N. Saidatin, D. H. Nugroho, I. T. Adhi, and T. Surabaya, “Implementasi Shape Feature dan K-Nearest Neighbor untuk Klasifikasi Tanda Tangan,” Pros. Semin. Nas. Sains dan Teknol. Terap., vol. 1, no. 1, pp. 155–162, 2020, [Online]. Available: http://ejurnal.itats.ac.id/sntekpan/article/view/1230

A. D. W. Sumari, M. R. Syahbana, and M. Mentari, “Pengenalan Jenis

Tanaman Mangga Berdasarkan Bentuk dan Tekstur Daun Menggunakan Kecerdasan Artifisial K-NearestNeighbor (KNN) dan Fusi Informasi,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 4, pp. 777–786, 2021, doi: 10.25126/jtiik.2021844392.

A. C. Vidyanti, I. Riati, and A. Ramadhanu, “Identification of Signature Authenticity Using Binary Extraction and K-nearest Neighbor Feature Methods,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 13, no. 2, pp. 274–279, 2024, doi: 10.32736/sisfokom.v13i2.2063.

Y. A. Sari, R. K. Dewi, and C. Fatichah, “Seleksi Fitur Menggunakan Ekstraksi Fitur Bentuk, Warna, Dan Tekstur Dalam Sistem Temu Kembali Citra Daun,” JUTI J. Ilm. Teknol. Inf., vol. 12, no. 1, p. 1, 2014, doi: 10.12962/j24068535.v12i1.a39.

M. Arief, “Klasifikasi Kematangan Buah Jeruk Berdasarkan Fitur Warna Menggunakan Metode SVM,” J. Ilmu Komput. dan Desain Komun. Vis., vol. 4, no. 1, pp. 9–16, 2019.




DOI: https://doi.org/10.30645/kesatria.v5i4.519

DOI (PDF): https://doi.org/10.30645/kesatria.v5i4.519.g514

Refbacks

  • There are currently no refbacks.


Published Papers Indexed/Abstracted By: