SISTEM PREDIKSI RASA BUAH JERUK MENGGUNAKAN METODE k-NEAREST NEIGHBOR

Authors

  • Abdullah Abdullah Universitas Islam Indragiri
  • Kurnia Sandi

DOI:

https://doi.org/10.46880/mtk.v7i2.457

Keywords:

Identifikasi, Buah Jeruk, k-Nearest Neighbor

Abstract

Every buyer always wants to get a good product, including in terms of buyers of citrus fruits, but many buyers do not know how to choose sweet oranges if they have not been tasted first, so buyers usually experience disappointment when buying citrus fruits. The purpose of this study is to assist buyers in choosing citrus fruits, the k-NN method in identifying the taste of citrus fruits. The results of testing with the k-fold cross validation method obtained a value of 96.55%, while testing with the holdout method obtained a value of 91.67%. The benefit of this research is that by using the application that was built, buyers can predict the taste of citrus fruits without having to taste them first.

References

X. Wang, T. Chen, D. Li, and S. Yu, “Processing Methods for Digital Image Data Based on the Geographic Information System,” Complexity, vol. 2021, p. 2319314, 2021, doi: 10.1155/2021/2319314.

K. Taunk, S. De, S. Verma, and A. Swetapadma, “A Brief Review of Nearest Neighbor Algorithm for Learning and Classification,” in 2019 International Conference on Intelligent Computing and Control Systems (ICCS), 2019, pp. 1255–1260, doi: 10.1109/ICCS45141.2019.9065747.

M. Neves, V. Trombin, F. Lopes, R. Kalaki, and P. Milan, “Nutritional benefits of oranges,” 2011, p. 116.

S. Kannadhasan, “Research Issues on Digital Image Processing For Various Applications in this World,” Jan. 2014.

P. Bangun, M. Sihombing, P. Studi, T. Informatika, and S. Utara, “Pengolahan citra untuk identifikasi kematangan buah jeruk dengan menggunakan metode backpropagation berdasarkan nilai hsv,” J. Tek. Inform. Kaputama, vol. 5, no. 1, pp. 85–91, 2021.

R. Enggar Pawening, W. Ja, and F. Shudiq, “Klasifikasi Kualitas Jeruk Lokal Berdasarkan Tekstur dan Bentuk Menggunakan Metode k-Nearest Neighbor (k-NN),” J. Ilmu Komput. dan Desain Komun. Vis., vol. 1, no. 1, pp. 10–17, 2020.

R. Rahmadewi, G. L. Sari, and H. Firmansyah, “Pendeteksian Kematangan Buah Jeruk Dengan Fitur Citra Kulit Buah Menggunakan Transformasi Ruang Warna HSV,” JTEV (Jurnal Tek. Elektro dan Vokasional), vol. 5, no. 1.1, pp. 166–171, 2019.

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.

N. C. S. Reddy, K. S. Prasad, and A. Mounika, “Classification Algorithms on Datamining : A Study,” Int. J. Comput. Intell. Res., vol. 13, no. 8, pp. 2135–2142, 2017.

N. Ibraheem, M. Hasan, R. Z. Khan, and P. Mishra, “Understanding Color Models: A Review,” ARPN J. Sci. Technol., vol. 2, no. 3, pp. 265–275, Jan. 2012.

M. Mukherjee, “Object-Oriented Analysis and Design,” Int. J. Adv. Eng. Manag., vol. 1, no. 1, p. 18, 2016, doi: 10.24999/ijoaem/01010003.

J. Brownlee, “K-Nearest Neighbors for Machine Learning,” Machine Learning Mastery, 2016. .

P. Galdi and R. Tagliaferri, “Data Mining: Accuracy and Error Measures for Classification and Prediction,” in Reference Module in Life Sciences, no. January, Elsevier, 2018, pp. 1–14.

S. Raschka, Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning. 2018.

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Published

10-09-2021

How to Cite

[1]
A. Abdullah and K. Sandi, “SISTEM PREDIKSI RASA BUAH JERUK MENGGUNAKAN METODE k-NEAREST NEIGHBOR”, METHODIKA, vol. 7, no. 2, pp. 7–13, Sep. 2021.