Classification Of Tomato Maturity Levels Based on RGB And HSV Colors Using KNN Algorithm

Lidya Ningsih; Putri Cholidhazia

doi:10.31004/riggs.v1i1.10

Authors

Lidya Ningsih Ilmu Komputer, Matematika dan Ilmu Pengetahuan Alam, Institut Pertanian Bogor
Putri Cholidhazia Department Informastika, Universitas Al Azhar Indonesia

DOI:

https://doi.org/10.31004/riggs.v1i1.10

Keywords:

Tomatoes, K-Nearest Neighbor, Euclidean Distance, Red Green Blue (RGB), Hue Saturation Value (HSV)

Abstract

Tomatoes (Lycopersiconeculentum Mill) are vegetables that are widely produced in tropical and subtropic areas. According to (Harllee) tomatoes are grouped into 6 levels of maturity, namely green, breakers, turning, pink, light red, and red. One way that can be used to classify the level of maturity of tomatoes in the field of informatics is to utilize digital image processing techniques. This study classifies the maturity of tomatoes using K-Nearest Neighbor (KNN) based on the Red Green Blue and Hue Saturation Value color features. The KNN algorithm was chosen as a classification algorithm because KNN is quite simple with good accuracy based on the minimum distance using Euclidean Distance. The research conducted received the highest accuracy result of 91.25% at the value of K = 7 with the test data 80. This shows that the KNN algorithm successfully classified the maturity of tomatoes by utilizing the color image of RGB and HSV.

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References

R. Pratama et al., “DETEKSI KEMATANGAN BUAH TOMAT BERDASARKAN FITUR WARNA MENGGUNAKAN METODE TRANSFORMASI RUANG WARNA HIS,” JIKO (Jurnal Inform. dan Komputer), vol. 2, no. 2, pp. 81–86, 2019, doi: 10.33387/jiko p-ISSN:

S. Aprilisa and Sukemi, “Klasifikasi Tingkat Kematangan Buah Tomat Berdasarkan Fitur Warna Menggunakan K-Nearest Neighhbor,” in Prosiding Annual Research Seminar, 2019, vol. 5, no. 1, pp. 978–979.

N. Astrianda, “Klasifikasi Kematangan Buah Tomat Dengan Variasi Model Warna Menggunakan Support Vector Machine,” VOCATECH Vocat. Educ. Technol. J., vol. 1, no. 2, pp. 45–52, 2020, doi: 10.38038/vocatech.v1i2.27.

S. Kusumaningtyas and R. A. Asmara, “Identifikasi Kematangan Buah Tomat Berdasarkan Warna Menggunakan Metode Jaringan Syaraf Tiruan (Jst),” J. Inform. Polinema, vol. 2, no. 2, p. 72, 2016, doi: 10.33795/jip.v2i2.59.

S. Y. Riska and P. Subekti, “Klasifikasi Level Kematangan Buah Tomat Berdasarkan Fitur Warna Menggunakan Multi-Svm,” J. Ilm. Inform., vol. 1, no. 1, pp. 39–45, 2016, doi: 10.35316/jimi.v1i1.442.

M. A. Anggriawan, M. Ichwan, and D. B. Utami, “Pengenalan Tingkat Kematangan Tomat Berdasarkan Citra Warna Pada Studi Kasus Pembangunan Sistem Pemilihan Otomatis,” J. Tek. Inform. dan Sist. Inf., vol. 3, no. 3, pp. 550–564, 2017, doi: 10.28932/jutisi.v3i3.688.

D. Wandi, F. Fauziah, and N. Hayati, “Deteksi Kelayuan Pada Bunga Mawar dengan Metode Transformasi Ruang Warna HSI Dan HSV,” STRING (Satuan Tulisan Ris. dan Inov. Teknol., vol. 5, no. 3, p. 333, 2021, doi: 10.30998/string.v5i3.8464.

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

S. Sanjaya, M. L. Pura, S. K. Gusti, F. Yanto, and F. Syafria, “K-Nearest Neighbor for Classification of Tomato Maturity Level Based on Hue, Saturation, and Value Colors,” Indones. J. Artif. Intell. Data Min., vol. 2, no. 2, p. 101, 2019, doi: 10.24014/ijaidm.v2i2.7975.

USDA, “United States Standards for Grades of Fresh Tomatoes,” United States Dep. Agric., vol. 1991, no. January, pp. 1–13, 1991.

A. M. Priyatno, “The Application of HAAR Wavelet and Backpropagation for Diabetic Retinopathy Classification Based on Eye Retina Image,” Int. J. Sci. Eng. Inf. Technol., vol. 03, no. 02, pp. 139–142, 2019, [Online]. Available: https://journal.trunojoyo.ac.id/ijseit/article/view/4536

H. Prabowo, “Deteksi Kondisi Kematangan Buah Jeruk Berdasarkan Kemiripan Warna Pada Ruang Warna RGB Berbasis Android,” J. Elektron. Sist. Inf. dan Komput., vol. 3, no. 2, pp. 9–19, 2017.

L. Farokhah, “Implementasi K-Nearest Neighbor untuk Klasifikasi Bunga Dengan Ekstraksi Fitur Warna RGB,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 6, p. 1129, 2020, doi: 10.25126/jtiik.2020722608.

S. Kolkur, D. Kalbande, P. Shimpi, C. Bapat, and J. Jatakia, “Human Skin Detection Using RGB, HSV and YCbCr Color Models,” in Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016), 2017, vol. 137, pp. 324–332. doi: 10.2991/iccasp-16.2017.51.

P. LI, Y. HUANG, and K. YAO, “Multi-algorithm Fusion of RGB and HSV Color Spaces for Image Enhancement,” in 2018 37th Chinese Control Conference (CCC), Jul. 2018, vol. 2018-July, pp. 9584–9589. doi: 10.23919/ChiCC.2018.8483674.

M. H. Malik, T. Zhang, H. Li, M. Zhang, S. Shabbir, and A. Saeed, “Mature Tomato Fruit Detection Algorithm Based on improved HSV and Watershed Algorithm,” IFAC-PapersOnLine, vol. 51, no. 17, pp. 431–436, 2018, doi: 10.1016/j.ifacol.2018.08.183.

A. M. Priyatno, F. M. Putra, P. Cholidhazia, and L. Ningsih, “Combination of extraction features based on texture and colour feature for beef and pork classification,” J. Phys. Conf. Ser., vol. 1563, no. 1, p. 012007, Jun. 2020, doi: 10.1088/1742-6596/1563/1/012007.

C. Chen, A. Dantcheva, and A. Ross, “Automatic facial makeup detection with application in face recognition,” in 2013 International Conference on Biometrics (ICB), Jun. 2013, no. April 2018, pp. 1–8. doi: 10.1109/ICB.2013.6612994.

M. Reza Noviansyah, T. Rismawan, D. Marisa Midyanti, J. Sistem Komputer, and F. H. MIPA Universitas Tanjungpura Jl Hadari Nawawi, “Penerapan Data Mining Menggunakan Metode K-Nearest Neighbor Untuk Klasifikasi Indeks Cuaca Kebakaran Berdasarkan Data Aws (Automatic Weather Station) (Studi Kasus: Kabupaten Kubu Raya),” J. Coding, Sist. Komput. Untan, vol. 06, no. 2, pp. 48–56, 2018.

T. Setiyorini and R. T. Asmono, “PENERAPAN METODE K-NEAREST NEIGHBOR DAN INFORMATION GAIN PADA KLASIFIKASI KINERJA SISWA,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 5, no. 1, pp. 7–14, Jun. 2019, doi: 10.33480/jitk.v5i1.613.

F. Shidiq, “Penerapan Metode K-Nearest Neighbor (KNN) Untuk Menentukan Ikan Cupang Dengan Ekstraksi Fitur Ciri Bentuk Dan Canny,” Innov. Res. Informatics, vol. 3, no. 2, pp. 39–46, 2021, doi: 10.37058/innovatics.v3i2.3093.

A. M. Argina, “Penerapan Metode Klasifikasi K-Nearest Neigbor pada Dataset Penderita Penyakit Diabetes,” Indones. J. Data Sci., vol. 1, no. 2, pp. 29–33, 2020, doi: 10.33096/ijodas.v1i2.11.

A. M. Priyatno, “Spammer Detection Based on Account, Tweet, and Community Activity on Twitter,” J. Ilmu Komput. dan Inf., vol. 13, no. 2, pp. 97–107, Jul. 2020, doi: 10.21609/jiki.v13i2.871.