Penerapan Fitur Warna dan Tekstur untuk Identifikasi Kerusakan Mutu Biji Kopi Arabika (Coffea Arabica) di Kabupaten Bondowoso
DOI:
https://doi.org/10.32815/jitika.v15i2.593Keywords:
kerusakan mutu, biji kopi arabika, warna, GLCM, BackpropagasiAbstract
Plantation crops are also a source of foreign exchange Indonesia is coffee. There are only two types of coffee that have economic value for cultivation, namely Arabica coffee and Robusta coffee. Bondowoso is a district in East Java that develops Arabica coffee. The problem is that farmers still use direct observation (manual) on each coffee bean to determine the quality of coffee beans so that this research is expected to be able to assist farmers in sorting the damage to the quality of coffee beans based on color and texture. The features used are color features and GLCM texture features at 0̊ and 45̊ angles. The total number of data is 198. The Backpropagation method is able to classify quality damage to Arabica coffee beans with a training accuracy rate of 100% and a testing accuracy rate of 97.5% at a learning rate variation of 0.5.
Downloads
References
Effendi, M., Fatasya, U., & Effendi, U. (2017). Identifikasi Jenis dan Mutu Kopi Menggunakan Pengolahan Citra Digital dengan Metode Jaringan Syaraf Tiruan. Jurnal Ilmiah Teknologi Pertanian AGROTECHNO, 2(1), 140–146.
Ega Ash Yokawati, Y., & Wachjar, A. (2019). Pengelolaan Panen dan Pascapanen Kopi Arabika (Coffea arabica L.) di Kebun Kalisat Jampit, Bondowoso, Jawa Timur. Buletin Agrohorti, 7(3), 343–350. https://doi.org/10.29244/agrob.v7i3.30471
Fitri, Z. E., Nuhanatika, U., Madjid, A., & Imron, A. M. N. (2020). Penentuan Tingkat Kematangan Cabe Rawit (Capsicum frutescens L.) Berdasarkan Gray Level Co-Occurrence Matrix. Jurnal Teknologi Informasi dan Terapan, 7(1), 1–5. https://doi.org/10.25047/jtit.v7i1.121
Fitri, Z. E., Rizkiyah, R., Madjid, A., & Imron, A. M. N. (2020). Penerapan Neural Network untuk Klasifkasi Kerusakan Mutu Tomat. Jurnal Rekayasa Elektrika, 16(1), 44–49. https://doi.org/10.17529/jre.v16i1.15535
Ikhsan, D., Utami, E., & Wibowo, F. W. (2020). Metode Klasifikasi Mutu Greenbean Kopi Arabika Lanang Dan Biasa Menggunakan K-Nearest Neighbor Berdasarkan Bentuk. Jurnal Ilmiah SINUS, 18(2), 1. https://doi.org/10.30646/sinus.v18i2.456
Nanda Imron, A. M., & Fitri, Z. E. (2019). A Classification of Platelets in Peripheral Blood Smear Image as an Early Detection of Myeloproliferative Syndrome Using Gray Level Co-Occurence Matrix. Journal of Physics: Conference Series, 1201(1). https://doi.org/10.1088/1742-6596/1201/1/012049
Nasution, T. H., & Andayani, U. (2017). Recognition of Roasted Coffee Bean Levels using Image Processing and Neural Network. Journal of Physics: Conference Series, 180(1), 1–8. https://doi.org/10.1088/1742-6596/755/1/011001
Pizzaia, J. P. L., Salcides, I. R., Almeida, G. M. De, Contarato, R., & Almeida, R. De. (2019). Arabica coffee samples classification using a Multilayer Perceptron neural network. 2018 13th IEEE International Conference on Industry Applications, INDUSCON 2018 - Proceedings, December 2019, 80–84. https://doi.org/10.1109/INDUSCON.2018.8627271
Rahardjo, P. (2012). Kopi: Panduan Budi Daya dan Pengolahan Kopi Arabika dan Robusta (1 ed.). Penebar Swadaya.
Additional Files
Published
How to Cite
Issue
Section
License
Upon acceptance for publication, authors transfer copyright of their article to Jurnal Ilmiah Teknologi Informasi Asia. This includes the rights to reproduce, transmit, and translate the material in any form or medium.
While the editorial board endeavors to ensure accuracy, they accept no responsibility for the content of articles or advertisements. Liability rests solely with the respective authors and advertisers.
Website material is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Under this license, users are free to share and adapt the material for any purpose, including commercial use, provided license terms are met. These freedoms are irrevocable by the licensor under such conditions.