CLASSIFICATION OF CAVENDISH BANANA MATURITY LEVEL USING CONVOLUTIONAL NEURAL NETWORK (CNN) WITH VGG-19 MODEL

Authors

  • Aditya Dwi Putro Wicaksono Fakultas Informatika, Institut Teknologi Telkom Purwokerto
  • Arif Amrulloh Fakultas Informatika, Institut Teknologi Telkom Purwokerto

DOI:

https://doi.org/10.31961/positif.v9i2.1778

Keywords:

Convolutional Neural Network, VGG-19, Maturity Level, Cavendish Bananas

Abstract

Bananas found in Cavendish Banana Gardens Purbalingga Regency have different levels of maturity and quality, as a local fruit that has high economic value and has a market potential that is still wide open, Cavendish bananas are one of the most reliable fruit commodities in Indonesia[1]. The government through the National Standardization Agency sets standards for bananas, maintaining the quality of bananas. The purpose of this study was to analyze the influence of light and image quality in classifying the ripeness level of bananas based on the color characteristics of bananas in the Cavendish Banana Garden, Banyumas Regency, Central Java according to SNI 7422:2009[2]. In this study the authors classify the maturity level of cavendish bananas using the Convolutional Neural Network with the Vgg-19 Model, VGG-19 is used to categorize the maturity level of cavendish bananas and the reason for choosing VGG-19 is because VGGNet is deeper and more reliable architecture for ImageNet technology.The author is also interested in learning how accurate the VGG-19 model is. With a total of 9,000 datasets, 80% of which are training data, 10% are validation data, and 10% are test data, The accuracy obtained for epochs 32, 64 and 96 varies. The accuracy results obtained using VGG-19 were 97% at epochs 32, 64 and 96

Downloads

Download data is not yet available.

References

I. Najiah, I. Hariyanti, A. R. Sanjaya, A. R. Sanjaya, K. P. Celak, and D. K. Pisang, “Deteksi Jenis Dan Kematangan Pisang Menggunakan Metode Extreme Learning,” Responsif, vol. 2, no. 2, pp. 232–242, 2020.

https://id.wikipedia.org, “Pisang cavendish,” https://id.wikipedia.org, 2021. https://id.wikipedia.org/wiki/Pisang_cavendish (accessed Jun. 03, 2021).

A. D. Putro and A. Hermawan, “Pengaruh Cahaya dan Kualitas Citra dalam Klasifikasi Kematangan Pisang Cavendish Berdasarkan Ciri Warna Menggunakan Artificial Neural Network,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 21, no. 1, pp. 215–228, 2021, doi: 10.30812/matrik.v21i1.1396.

A. Dwi and P. Wicaksono, “Klasifikasi Tingkat Kematangan , Kualitas dan Jenis Buah Pisang Berdasarkan Ciri Warna dan Bentuk Menggunakan Artificial Neural Networks,” vol. 07, no. 02, pp. 91–98, 2022.

Y. Zhang, J. Gao, and H. Zhou, “Breeds Classification with Deep Convolutional Neural Network,” ACM Int. Conf. Proceeding Ser., 2020, doi: 10.1145/3383972.3383975.

I. SABILLA, AHMAD, “Arsitektur Convolutional Neural Network (Cnn) Untuk Klasifikasi Jenis Dan Kesegaran Buah Pada Neraca Buah,” Tesis, no. 201510370311144, pp. 1–119, 2020, [Online]. Available: https://repository.its.ac.id/73567/1/05111850010020-Master_Thesis.pdf.

S. G. and K. Sarkar, “Rice Leaf Diseases Classification Using CNN with Transfer Learning,” IEEE Calcutta Conf. CALCON, pp. 230–236, 2020, doi: 10.1109/CALCON49167.2020.9106423.

and I. I. A. Hidayat, U. Darusalam, “Detection of Disease on Corn Plants Using Convolutional Neural Network Methods,” J. Ilmu Komput. dan Inf, vol. 12, no. 1, p. 51, 2020, doi: 10.21609/jiki.v12i1.695.

and A. S. E. Anggiratih, S. Siswanti, S. K. Octaviani, “Klasifikasi Penyakit Tanaman Padi Menggunakan Model Deep Learning Efficientnet B3 dengan Transfer Learning,” J. Ilm. SINUS, vol. 19, no. 1, p. 75, 2021, doi: 10.30646/sinus.v19i1.526.

F. Emmert-streib, Z. Yang, H. Feng, S. Tripathi, and F. Emmert-streib, “An Introductory Review of Deep Learning for Prediction Models With Big Data,” vol. 3, no. February, pp. 1–23, 2020, doi: 10.3389/frai.2020.00004.

Downloads

Published

2023-12-27

How to Cite

Wicaksono, A. D. P., & Amrulloh, A. (2023). CLASSIFICATION OF CAVENDISH BANANA MATURITY LEVEL USING CONVOLUTIONAL NEURAL NETWORK (CNN) WITH VGG-19 MODEL. POSITIF : Jurnal Sistem Dan Teknologi Informasi, 9(2), 107–114. https://doi.org/10.31961/positif.v9i2.1778

Issue

Vol. 9 No. 2 (2023): Positif : Jurnal Sistem dan Teknologi Informasi

Section

Articles

License

Copyright (c) 2023 Aditya Dwi Putro Wicaksono, Arif Amrulloh

Creative Commons License

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

Authors who publish with Positif : Jurnal Sistem dan Teknologi Informasi agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.

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