METODE KLASIFIKASI MUTU JAMBU BIJI MENGGUNAKAN KNN BERDASARKAN FITUR WARNA DAN TEKSTUR

https://doi.org/10.22146/teknosains.26972

Taftyani Yusuf Prahudaya(1), Agus Harjoko(2*)

(1) 
(2) Gadjah Mada University
(*) Corresponding Author

Abstract


Guava (Psidium guajava L.) is a fruit that has many health benefits. Guava also has commercial value in Indonesia and has a large market share. This indicates that the commodity of guava has been consumed by society extensively. This time the sorting process is still done manually which still has many shortcomings. This classification gives the classification results are less accurate and inconsistent due to the carelessness of humans. Grading process in the marketing sector is essential. Improper grading potentially detrimental to farmers because all the fruit quality were priced the same. Therefore, we need a consistent classification system.The system uses image processing to extract the color and texture features of guava. As a quality classification KNN method (K-Nearest Neighbor) is used. This system will classify guava into four quality classes, namely the super class, class A, class B, and external quality. KNN designed with input 7 features extraction which is the average value of RGB (Red, Green, and Blue), total defect area, and the GLCM value (entropy, homogeneity, and contrast) with the 4 outputs of quality. From the test results showed that the classification method is able to classify the quality of guava. The highest accuracy is obtained in testing K = 3 with 91.25% accuracy rate.


Keywords


Classification; Digital image processing; Guava; KNN

Full Text:

PDF


References

Badan Standarisasi Nasional 2009. Jambu Biji, Badan Standarisasi Nasional Jakarta. [2]Sari, N., 2004, Pendugaan Biji Kopi Utuh, Bjji Kopi Pecah, Biji Kopi Berlubang dan Benda Asing Untuk Evaluasi Mutu Kopi Dencan Pengolahan Citra dan Metode Fuzzy. Skripsi Jurusan Teknik Pertanian Fakultas Pertanian IPB, Bogor [3] Prasetyo, E., 2011, Pengolahan Citra Digital dan Aplikasinya Menggunakan Matlab, Andi Offset, Yogyakarta. [4]Tuceryan, M., & Jain, A.K. 1998. Texture analysis. Handbook of pattern recognition and computer vision, World Scientific Publishing Co., Michigan [5]Arham Z, Ahmad U, Suroso 2004, Evaluasi Mutu Jeruk Nipis (Citrus aurantifolia Swingle) dengan Pengolahan Citra Digital dan Jaringan Syaraf Tiruan, Prosiding Semiloka Teknologi Simulasi dan Komputasi serta Aplikasi, Bogor [6]Prianggono J., Kudang B., Hadi K. P., Ahmad U., dan Subratas I.D.M. 2005 Algoritma Pengolahan Citra Untuk Deteksi Jeruk Lemon (Citrus medica) Menggunakan Kamera Online Jurnal Keteknikan Pertanian, Bogor. [7] Perwiranto, H., 2012, Sistem Klasifikasi Mutu Buah Tomat Menggunakan Pengolahan Citra Digital dan Jaringan Saraf Tiruan, Skripsi, Jurusan Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta [8]Khoshroo, A., Keyhani1, A., Zoroofi, R.A., Rafiee1, S., Zamani, Z., dan Alsharif, M.R., 2009. Classification of Pomegranate Fruit using Texture Analysis of MR Images, Agricultural Engineering International, Volume XI, http://www.cigrjournal.org/index.php/Ejounral/article/viewFile/1182/1166 [9] Gonzalez, R.C., Richard E.W., dan Steven L.E. 2009. Digital Image Processing Using MATLAB. Vol. 2. Knoxville: Gatesmark Publishing. [10]Prasetyo, E., 2014, Data Mining Mengolah Data Menjadi Informasi Menggunakan Matlab, Andi Offset, Yogyakarta.



DOI: https://doi.org/10.22146/teknosains.26972

Article Metrics

Abstract views : 1428 | views : 3334

Refbacks

  • There are currently no refbacks.




Copyright (c) 2017 Taftyani Yusuf Prahudaya, Agus Harjoko

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