Ripeness identification of chayote fruits using HSI and LBP feature extraction with KNN classification


Siska Anraeni(1*); Erika Riski Melani(2); Herman Herman(3);

(1) Universitas Muslim Indonesia
(2) Universitas Muslim Indonesia
(3) Universitas Muslim Indonesia
(*) Corresponding Author

  

Abstract


This study aims to build a system to identify the ripeness level of chayote that can be done easily and without damaging the quality of the chayote. This study employs digital image processing technology using Hue Saturation Intensity color feature extraction and texture feature extraction of Local Binary Pattern with K-Nearest Neighbor classification so that the process of identifying the ripeness level of chayote will be easier and more effective. This study uses 100 image datasets and is carried out by taking photos of chayote. The stages in this study include the input of chayote images followed by the image pre-processing stage. Next is feature extraction which is divided into three scenarios, namely HSI feature extraction, LBP feature extraction and a combination of the two feature extractions. The final stage is to classify objects that are closest to the object being tested using the KNN method. By determining the value of K in the KNN classification method, the results show that the use of the Chebyshev distance calculation model in LBP feature extraction with K = 5 is a test that has the best accuracy of 90%.

Keywords


hue saturation intensity; local binary patern; K-Nearest Neighbor; chayote fruits

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 350 times
PDF view: 170 times 		     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v14i2.1153.150-159 		  

Cite

How to cite item

References


Arief Prahasta Soedarya, Agribisnis labu siam. Bandung: CV. Pustaka Grafika, 2009.

Badan Pusat Statistik Sulawesi Selatan, “Statistik tanaman hortikultura provinsi Sulawesi Selatan 2020,” 2021. doi: 5204003.73.

R. Munawassalmiah, Hajrah, and L. Rijai, “Observasi klinik ekstrak labu siam (Sechium edule) sebagai antihipertensi,” Proceeding Mulawarman Pharm. Conf., vol. 8, pp. 128–135, Dec. 2018, doi: 10.25026/mpc.v8i1.314.

R. Lira, J. Heller, and J. Engels, Chayote, Sechium edule (Jacq.) Sw. 1996.

Mega Wulan Sari S., “Pengaruh jumlah asam sitrat dan agar-agar terhadap sifat organoleptik manisan bergula puree labu sam (Sechium edule),” E-Journal Boga, vol. Vol 3 No 1, 2019, [Online]. Available: https://ejournal.unesa.ac.id/index.php/jurnal-tata-boga/article/view/6531

A. N. Dzulhijjah, S. Anraeni, and Sugiarti, “Klasifikasi kematangan citra labu siam menggunakan metode KNN (K-Nearest Neighbor) dengan ekstraksi fitur HSV (Hue, Saturation, Value),” Bul. Sist. Inf. dan Teknol. Islam, vol. Vol.2, No., 2021, [Online]. Available: http://jurnal.fikom.umi.ac.id/index.php/BUSITI/article/view/808

A. I. Thoriq, M. H. Zuhri, P. Purwanto, P. Pujiono, and H. A. Santoso, “Classification of banana maturity levels based on skin image with HSI color space transformation features using the K-NN Method,” J. Dev. Res., vol. 6, no. 1, pp. 11–15, May 2022, doi: 10.28926/jdr.v6i1.200.

S. R. Hidiya and M. E. Lasulika, “Fitur ekstraksi LBP untuk mengidentifikasi kematangan tomat sayur menggunakan metode K-Nearest Neighbor,” J. Cosphi, vol. 3, no. 1, 2019.

Hendry J, Color Conversion - RGB to HSI. Yogyakarta: EE & IT of UGM, 2012.

R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB. Pearson Prentice Hall, 2004. [Online]. Available: https://books.google.co.id/books?id=YYuJQgAACAAJ

A. Firdaus, B. Purnama, and U. N. Wisesty, “Klasifikasi kendaraan di jalan tol dengan menerapkan metode local binary pattern dan linear discriminant analysis,” 2016, pp. 285–296. doi: 10.21108/INDOSC.2016.158.

T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognit., vol. 29, no. 1, pp. 51–59, 1996, doi: https://doi.org/10.1016/0031-3203(95)00067-4.

M. Heikkilä, M. Pietikäinen, and C. Schmid, “Description of interest regions with local binary patterns,” Pattern Recognit., vol. 42, pp. 425–436, 2009.

T. Lindahl, “Study of Local Binary Patterns,” 2007.

S. Fekri Ershad, “Texture classification approach based on combination of edge & co-occurrence and local binary pattern,” Mar. 2012.

A. A. P. B. Dwi, “Identifikasi sub-fosil gigi geraham pada manusia berbasis pengolahan citra digital menggunakan metode local binary pattern (LBP) dan klasifikasi learning vector quantization (LVQ),” 2018.

F. Marleny, Pengolahan Citra Digital Menggunakan Python. 2022.

Ketutrare, “Algoritma K-Nearest Neighbor dan contoh soal,” Ketutrare, 2018. https://www.ketutrare.com/2018/11/algoritma-k-nearest-neighbor-dan-contoh-soal.html

I. A. Angreni, S. A. Adisasmita, M. I. Ramli, and S. Hamid, “Pengaruh nilai k pada metode K-Nearest Neighbor (KNN) terhadap tingkat akurasi identifikasi kerusakan jalan,” Rekayasa Sipil, vol. 7, no. 2, p. 63, Jan. 2019, doi: 10.22441/jrs.2018.v07.i2.01.

S. Anraeni, D. Indra, D. Adirahmadi, S. Pomalingo, Sugiarti, and S. H. Mansyur, “Strawberry ripeness identification using feature extraction of RGB and K-Nearest Neighbor,” in 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT), 2021, pp. 395–398. doi: 10.1109/EIConCIT50028.2021.9431854.

M. F. Abdi, Kusrini, and M. P. Kurniawan, “Local binary pattern untuk ekstraksi tekstur gambar wajah menggunakan masker dan tanpa masker,” Technologia, vol. 13, No. 2, 2022, doi: http://dx.doi.org/10.31602/tji.v13i2.6275.

I. Iswanto, T. Tulus, and P. Sihombing, “Comparison of distance models on K-Nearest Neighbor algorithm in stroke disease detection,” Appl. Technol. Comput. Sci. J., vol. 4, no. 1, pp. 63–68, Jul. 2021, doi: 10.33086/atcsj.v4i1.2097.

N. A. dan Yuita Sari dan Randy Wihandika, “Pengenalan emosi berdasarkan ekspresi mikro menggunakan metode Local Binary Pattern,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 10, pp. 3230–3238, 2018, [Online]. Available: httpsj-ptiik.ub.ac.idindex.phpj-ptiikarticleview2594.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Siska Anraeni, Erika Riski Melani, Herman Herman

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.