Identify the Color and Shape of Eggplant Using Back Propagation Method
Abstract
Currently, artificial neural networks are being developed as a tool that can help with human tasks. The main purpose of this study is to identify the structure of an eggplant, and to distinguish the type of eggplant. This study empirically tested the shape and color of several eggplants using the back propagation neural network learning method. The data is obtained from an image that will be entered into the program. The data used in the identification process are two photos containing two types of eggplant, the first eggplant is green and round and the next eggplant is purple and oval. The results of the identification process using this backpropagation from the tests that have been carried out previously, the highest calculation results obtained with the best results using a learning rate of 0.7 and epoch iterations of 500 and producing an accuracy of 73.33%.
Downloads
References
Dinpertanpangan Kab. Demak, “Teknik Sederhana Budidaya Tanaman Terong (Solanum Melongena)”, 18 Agustus 2021. https://dinpertanpangan.demakkab.go.id/?p=3133
F. Rizal, A.Wijaya, U. R. Hidayat, “Penerapan Algoritma Backpropagation Untuk Klasifikasi Jenis Buah Rambutan Berdasarkan Fitur Tekstur Daun”, Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM), Vol 1 No 2, Oktober 2020, ISSN: 2722-435X, pp 1-8. 2020.
M. Astiningrum , M. Z.Abdullah , P. P. Sari, “Identifikasi Kualitas Terung Ungu berdasarkan Warna dan Tekstur menggunakan metode Jaringan Saraf Tiruan”, Seminar Informatika Aplikatif Polinema (SIAP) 2020, ISSN 2460-1160, pp. 554-569, 2020.
http://jurnalti.polinema.ac.id/index.php/SIAP/article/view/860
E. P. Cynthia, E. Ismanto, “Jaringan Syaraf Tiruan Algoritma Backpropagation Dalam Memprediksi Ketersediaan Komoditi Pangan Provinsi Riau”, RABIT (Jurnal Teknologi dan Sistem Informasi Univrab), VOL. 2 No. 2, ISSN CETAK : 2477-2062. ISSN ONLINE : 2502-891X, pp. 196-209, Juli 2017. https://media.neliti.com/media/publications/279914-jaringan-syaraf-tiruan-algoritma-backpro-f0165b57.pdf
D. L. Rahakbauw, “Analisis Jaringan Saraf Tiruan Back propagation Terhadap Peramalan Nilai Tukar Mata Uang Rupiah Dan Dolar”, Jurnal Barekeng Vol. 8 No. 2 pp. 27 – 32, 2014. https://media.neliti.com/media/publications/277544-analisis-jaringan-saraf-tiruan-backpropa-23e7cdc8.pdf.
A. F. Suahati, A. A. Nurrahman, O Rukmana, “Penggunaan Jaringan Syaraf Tiruan– Backpropagation dalam Memprediksi Jumlah Mahasiswa Baru”, Jurnal Media Teknik & Sistem Industri, Vol. 6, No. 1, e-issn: 2581-056, p-issn: 2581-0529, pp, 21–29, Maret 2022.
https://jurnal.unsur.ac.id/jmtsi/article/view/1589
F. Nugraha , B Irawan , D. M. Midyanti, “Deteksi Penyakit Pada Tanaman Jeruk Pontianak Dengan Metode Jaringan Saraf Tiruan Backpropagation”, Jurnal Coding, Sistem Komputer Untan, Volume 04, No.2 (2016), ISSN : 2338-493x, pp.76-85, 2016. https://jurnal.untan.ac.id/index.php/jcskommipa/article/view/14761/13054
S. Redjeki. “Perbandingan Algoritma Backpropagation dan K-Nearest Neighbor (K-NN)untuk Identifikasi Penyakit”, Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2013, Yogyakarta, ISSN: 1907 – 5022, pp. 1- ., 15 Juni 2013. https://journal.uii.ac.id/Snati/article/view/3040
Y. N. Sari. “Jaringan Syaraf Tiruan Backpropagation Untuk Memprediksi Luas Area Serangan Hama Pada Tanaman Bawang”, Tesis Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Negeri Semarang,2016. http://lib.unnes.ac.id/28054/1/4611412033.pdf
M. D. Yalidhan, “Implementasi Algoritma Backpropagation Untuk Memprediksi Kelulusan Mahasiswa”, Kumpulan jurnaL Ilmu Komputer (KLIK), Volume 05, No.02, ISSN:2406-7857, pp. 169-178, September 2018.
http://klik.ulm.ac.id/index.php/klik/article/view/152
Andrian. “Penerapan Algoritma Backpropagation Dan Principal Component Analysis Untuk Pengenalan Wajah”, Jurnal Teknovasi, Volume 01, Nomor 2, ISSN : 2355-701X, pp. 62 – 70, 2014.
Dio Very Hutabarat1, Solikhun2, M. Fauzan3, Agus Perdana Windarto4, Fitri Rizki, “Penerapan Algoritma Backpropagation dalam Memprediksi Hasil Panen Tanaman Sayuran”, BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer, Vol. 2, No. 1, Maret 2021, ISSN: 2722-0850, pp. 21-29, 2021.
E. Budianita, T. Ulfadhyani., F. Yanto., Pizaini, “Implementasi Algoritma Canny Dan Backpropagation Untuk Mengklasifikasi Jenis Tanaman Mangga”, Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI) 11, ISSN (Printed) : 2579-7271, ISSN (Online ) : 2579-5406, pp. 13-41, Pekanbaru,12 November 2019. http://ejournal.uin-suska.ac.id/index.php/SNTIKI/article/view/7774
J. R. Simanungkalit, Haviluddin, H. S. Pakpahan, N. Puspitasari dan M. Wati, “Algoritma Backpropagation Neural Network dalam Memprediksi Harga Komoditi Tanaman Karet”, ILKOM Jurnal Ilmiah, Vol. 12 No. 1, E-ISSN 2548-7779, , pp.32-38, April 2020
http://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/521/pdf
D. V. Hutabarat, Solikhun, M. Fauzan, A. P. Windarto, F. Rizki, ”Penerapan Algoritma Backpropagation dalam Memprediksi Hasil Panen Tanaman Sayuran”, BIOS :Jurnal Teknologi Informasi dan Rekayasa Komputer, Vol.2, No.1, Maret 2021, hlm. 21-29, ISSN: 2722-0850, 2021. https://bios.sinergis.org/index.php/bios/article/view/18/19
Copyright (c) 2022 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;
