Implementation of Deep Learning for Classification Type of Orange Using The Method Convolutional Neural Network

Irvan Denata, Tedy Rismawan, Ikhwan Ruslianto

Abstract


Orange is a type of fruit that is easily found in Sambas Regency. The types that are widely sold are Siam oranges, madu susu and susu. Each type of orange has a different quality and a different price. The price difference often results in fraud committed by traders against buyers to the detriment of the buyer. This is because differentiating types of oranges based on the appearance of the fruit does not have a standard. Therefore, in this study, a citrus fruit classification system was created based on images by implementing deep learning. The method of deep learning used in this research is Convolutional Neural Network (CNN) with AlexNet architecture. The types of oranges that will be observed are madu oranges, madu susu, and siam. The data used are 2250 images of oranges with each class totaling 750 images with a size of 227x227 pixels. The training data is 1575 images and the test data is 675 images. The training is carried out with a total of 10 epochs and each epoch will produce a model. System testing is carried out based on the model generated in the training process. Each model will be observed results in the form of accuracy which is calculated using a confusion matrix. The most optimal model was generated from training in epoch the 9th which resulted in an accuracy of 94.81%.

Keywords


convolutional neural network; orange; deep learning; alexnet

Full Text:

PDF

References


“BPS Kabupaten Sambas.” https://sambaskab.bps.go.id/dynamictable/2019/10/18/67/jumlah-tanaman-menghasilkan-dan-produksi-tanaman-buah-buahan-dan-sayuran-tahunan-di-kabupaten-sambas-tahun-2016-2018.html (accessed Sep. 01, 2021).

A. Santoso and G. Ariyanto, “IMPLEMENTASI DEEP LEARNING BERBASIS KERAS UNTUK PENGENALAN WAJAH,” Jurnal Teknik Elektro, vol. 18, no. 01, [Online]. Available: https://www.mathworks.com/discovery/convol

A. Kurniadi and M. Fal Sadikin, “Implementasi Convolutional Neural Network Untuk Klasifikasi Varietas Pada Citra Daun Sawi Menggunakan Keras Implementation of Neural Network Convolutionals For Classification of Variety on Image of Collards Meat Leaves Using The Keras,” vol. 4, no. 1, pp. 25–33, 2020, [Online]. Available: http://e-journal.unipma.ac.id/index.php/doubleclick

F. Felix, J. Wijaya, S. P. Sutra, P. W. Kosasih, and P. Sirait, “Implementasi Convolutional Neural Network Untuk Identifikasi Jenis Tanaman Melalui Daun,” Jurnal SIFO Mikroskil, vol. 21, no. 1, pp. 1–10, 2020.

S. Ilahiyah and A. Nilogiri, “Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia), vol. 3, no. 2, pp. 49–56, 2018.

W. Agustian, S. Setyaningsih, and Q. Arie, “Klasifikasi Buah Jeruk Menggunakan Metode Naive Bayes Berdasarkan Analisis Tekstur dan Normalisasi Warna,” Jurnal Online Mahasiswa (JOM) Bidang Ilmu Komputer/Informatika, vol. 2, no. 2, 2015.

J. Sanjaya and M. Ayub, “Augmentasi Data Pengenalan Citra Mobil Menggunakan Pendekatan Random Crop, Rotate, dan Mixup,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 6, no. 2, Aug. 2020, doi: 10.28932/jutisi.v6i2.2688.

A. Wedianto, H. Latipa Sari, and Y. Suzantri H, “ANALISA PERBANDINGAN METODE FILTER GAUSSIAN, MEAN DANMEDIAN TERHADAP REDUKSI NOISE,” Jurnal Media Infotama, vol. 12, Feb. 2016.

R. Venkatesan and B. Li, Convolution Neural Networks in Visual Computing A Concise Guide. Florida: CRC Press, 2017.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, [Online]. Available: http://code.google.com/p/cuda-convnet/

S. Khan, H. Rahmani, S. A. A. Shah, and M. Bennamoun, A Guide to Convolutional Neural Networks for Computer Vision, vol. 8, no. 1. Morgan publishers & cLaypool, 2018. doi: 10.2200/s00822ed1v01y201712cov015.

Y. Achmad, R. C. Wihandika, and C. Dewi, “Klasifikasi emosi berdasarkan ciri wajah wenggunakan convolutional neural network,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 3, no. 11, pp. 10595–10604, 2019.

M. Satria Wibawa, “Pengaruh Fungsi Aktivasi, Optimisasi dan Jumlah Epoch Terhadap Performa Jaringan Saraf Tiruan,” JURNAL SISTEM DAN INFORMATIKA, vol. 11, Nov. 2016.

I. W. S. E. Putra, A. Y. Wijaya, and R. Soelaiman, “Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101,” Jurnal Teknik ITS, vol. 5, no. 1, 2016, doi: 10.12962/j23373539.v5i1.15696.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge: MIT Press, 2016.

I. M. Erwin, E. Prakasa, and B. Sugiarto, “Kayu7Net : Identifikasi Dan Evaluasi F-Measure Citra Kayu Berbasis Deep Convolution Neural Network ( Dcnn ) Kayu7Net : Identification and F-Measure Evaluation Wood Image Based on Deep Convolution Neural Networks ( Dcnn ),” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. x, no. 30, pp. 1–10, 2019.




DOI: https://doi.org/10.31315/telematika.v18i3.5541

DOI (PDF): https://doi.org/10.31315/telematika.v18i3.5541.g4249

Refbacks

  • There are currently no refbacks.


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

Copyright of :
TELEMATIKA: Jurnal Informatika dan Teknologi Informasi
ISSN 1829-667X (print); ISSN 2460-9021 (online)


Dipublikasi oleh
Jurusan Teknik Informatika, UPN Veteran Yogyakarta
Jl. Babarsari 2 Yogyakarta 55281 (Kampus Unit II)
Telp: +62 274 485786
email: jurnaltelematika@upnyk.ac.id

 

Jurnal Telematika sudah diindeks oleh beberapa lembaga berikut:
 

 

 

 

 

Status Kunjungan Jurnal Telematika