PENGENALAN VARIETAS KOPI ARABIKA BERDASARKAN FITUR BENTUK
Abstract
Coffee is one of the most popular beverages in the world and has a different taste. One of the most famous types of coffee is arabica. This coffee has many varieties depend on the area of planting. Therefore, sometimes though both arabica coffee varieties, but may have different features such as differences in color, shape, or texture. So that sometimes farmers or coffee shop owners even people can make mistakes in recognizing arabica coffee varieties. This problem will affect the price determination of coffee, because each varieties have different prices. Therefore, needed a system that is able to recognize arabica coffee varieties accurately so it can be used as a second opinion in recognizing arabica coffee varieties. One of the technique that can be used is by imaging. This research begins with the pre-processing stage that is cropping the image manually. The next stage is feature extraction using the solidity features associated with convex hull. The last stage is classification using MultiLayer Perceptron and obtained 80% accuracy, 80% sensitivity, and 80% specificity.
Keywords
Full Text:
PDFReferences
R. H. M. Condori, J. H. C. Humari, C. E. Portugal-Zambrano, J. C. Gutiérrez-Cáceres, and C. A. Beltrán-Castañón, “Automatic classification of physical defects in green coffee beans using CGLCM and SVM,” in Proceedings of the 2014 Latin American Computing Conference, CLEI 2014, 2014.
R. G. Apaza, C. E. Portugal-Zambrano, J. C. Gutiérrez-Cáceres, and C. A. Beltrán-Castañón, “An approach for improve the recognition of defects in coffee beans using retinex algorithms,” in Proceedings of the 2014 Latin American Computing Conference, CLEI 2014, 2014.
E. M. De Oliveira, D. S. Leme, B. H. G. Barbosa, M. P. Rodarte, and R. G. F. Alvarenga Pereira, “A computer vision system for coffee beans classification based on computational intelligence techniques,” J. Food Eng., vol. 171, pp. 22–27, 2016.
Radi, M. Rivai, and M. H. Purnomo, “Combination of first and second order statistical features of bulk grain image for quality grade estimation of green coffee bean,” ARPN J. Eng. Appl. Sci., vol. 10, no. 18, pp. 8165–8174, 2015.
M. M. Sebatubun and M. A. Nugroho, “EKSTRAKSI FITUR CIRCULARITY UNTUK PENGENALAN VARIETAS KOPI ARABIKA,” vol. 4, no. 4, pp. 283–289, 2017.
I. D. E. K. Ratri, H. A. Nugroho, and T. B. Adji, “Pengenalan Keaslian Tanda Tangan dengan menggunakan Kombinasi Dynamic Time Warping (DTW) dan Polar Fourier Transform,” Universitas Gadjah Mada, 2015.
A. Susanto and A. Kadir, “Ekstraksi Fitur Bentuk dan Kontur,” in Pengolahan Citra, 2012, pp. 575–640.
Y. F. Riti, “Ekstraksi Ciri Untuk Klasifikasi Tepi Lesi dari Citra CT Scan Paru-paru,” 2017.
L. Noriega, “Multilayer Perceptron Tutorial.” 2005.
A. Nugroho, “Klasifikasi nodul tiroid berbasis ciri tekstur pada citra ultrasonografi,” Universitas Gadjah Mada, 2015.
DOI: http://dx.doi.org/10.26798/jiko.v3i2.117
Article Metrics
Abstract view : 729 timesPDF - 2069 times
Refbacks
- There are currently no refbacks.
Copyright (c) 2018 Maria Mediatrix Sebatubun, Erna Hudianti Pujiarini
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.