Classification of Coffee Bean Defects Using Gray-Level Co-Occurrence Matrix and K-Nearest Neighbor


Mila Jumarlis(1*); Mirfan Mirfan(2); Abdul Rachman Manga(3);

(1) STAIN Majene
(2) STMIK Handayani
(3) Univeristas Muslim Indonesia
(*) Corresponding Author

  

Abstract


Defects in coffee beans can significantly affect the quality of coffee production so that defects in coffee beans can cause a decreasing the level of coffee production. The purpose of this study is to implement the GLCM (gray-level co-occurrence matrix) and the K-NN (k-nearest neighbor) method on a web-based program and provided a website to detect coffee bean defects. This study uses the GLCM algorithm to extract the features of the coffee images and uses the K-NN algorithm to classify the defect level of coffee beans. The system development was built using Unified Modeling Language. The development of this website was utilized the programming structure of PHP, HTML, CSS, Javascript, Mozilla Firefox as a browser for the website and MySql for the database management systems. The results show that the system can provide the output in the form of a classification level of the defect level of the coffee bean images. Then, the accuracy of the coffee bean defect assessment was achieved by 90%. Finally, this study concluded that the proposed system could help the coffee farmers determine the defect level of the coffee beans using images input.

Keywords


Coffee beans; Digital Image; GLCM; Classification; K-NN

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 1091 times
PDF view: 327 times
     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v14i1.910.1-9
  

Cite

References


B. Marhaenanto, D. W. Soedibyo, and M. Farid, Penentuan lama Sangrai Kopi Terhadap Variasi Derajat Sangrai Menggunakan Model Warna Rgb Pada Pengolahan Citra Digital (Digital Image Processing), J. Agroteknologi, vol. 09, no. 02, pp. 110, 2015.

E. R. Arboleda, A. C. Fajardo, and R. P. Medina, An image processing technique for coffee black beans identification, 2018 IEEE Int. Conf. Innov. Res. Dev. ICIRD 2018, no. May, pp. 15, 2018.

R. Sistem, P. Citra, B. Jagung, T. Elektro, P. Magister, and U. Gunadarma, Penerapan Convolutional Neural Network Deep Learning dalam Pendeteksian Citra Biji Jagung Kering, vol. 1, no. 10, pp. 265271, 2021.

M. R. Tasya, B. S. W. A, and E. T. Luthfi, Klasifikasi Kualitas Kematangan Wortel Menggunakan Metode GLCM ( Gray Level Co-Occurrence Matrix ) Dan Neural Network, J. FATEKSA J. Teknol. dan Rekayasa, vol. 5, pp. 110, 2020.

F. F. Maulana and N. Rochmawati, Klasifikasi Citra Buah Menggunakan Convolutional Neural Network, J. Informatics Comput. Sci., vol. 01, pp. 104108, 2019.

S. Juliansyah and A. D. Laksito, Klasifikasi Citra Buah Pir Menggunakan Convolutional Neural Networks, J. Telekomun. dan Komput., vol. 11, no. 1, p. 65, 2021.

D. M. Asriny, S. Rani, and A. F. Hidayatullah, Orange Fruit Images Classification using Convolutional Neural Networks, IOP Conf. Ser. Mater. Sci. Eng., vol. 803, no. 1, 2020.

I. Wulandari, H. Yasin, and T. Widiharih, Klasifikasi Citra Digital Bumbu Dan Rempah Dengan Algoritma Convolutional Neural Network (Cnn), J. Gaussian, vol. 9, no. 3, pp. 273282, 2020.

Luqman Hakim, Z. Sari, and H. Handhajani, Klasifikasi Citra Pigmen Kanker Kulit Menggunakan Convolutional Neural Network, J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 379385, 2021.

P. D. Wananda, L. Novamizanti, and R. D. Atmaja, Sistem Deteksi Cacat Kayu dengan Metode Deteksi Tepi SUSAN dan Ekstraksi Ciri Statistik, ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 6, no. 1, p. 140, 2018.

C. J. Kuo et al., A Labor-Efficient GAN-based Model Generation Scheme for Deep-Learning Defect Inspection among Dense Beans in Coffee Industry, IEEE Int. Conf. Autom. Sci. Eng., vol. 2019-Augus, pp. 263270, 2019.

E. Alvansga, Pengenalan Tekstur Menggunakan Metode Glcm Serta Modul Nirkabel, Comput. J., pp. 7075, 2019.

Isman, A. Ahmad, and A. Latief, Perbandingan Metode KNN Dan LBPH Pada Klasifikasi Daun Herbal, vol. 1, no. 10, pp. 557564, 2021.

I. T. Sitorus, D. R. Simarmata, and I. Christinawati, Pengenalan Biji Kopi Arabika varietas Sigarar Utang Lintong Nihuta Berdasarkan Parameter Tekstur Menggunakan Machine Learning ( Studi Kasus : KSU POM Humbang Cooperative ), pp. 68, 2020.

C. Pinto, J. Furukawa, H. Fukai, and S. Tamura, Classification of Green coffee bean images basec on defect types using convolutional neural network (CNN), Proc. - 2017 Int. Conf. Adv. Informatics Concepts, Theory Appl. ICAICTA 2017, 2017.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Mila Jumarlis, Mirfan, Abdul Rachman Manga

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