CFP last date
20 August 2024
Reseach Article

Implementation of Citra Technology to Identify the Freshness of Shrimp for Consumption

by Aris Prayogo, Enny Itje Sela
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 48
Year of Publication: 2023
Authors: Aris Prayogo, Enny Itje Sela
10.5120/ijca2023923309

Aris Prayogo, Enny Itje Sela . Implementation of Citra Technology to Identify the Freshness of Shrimp for Consumption. International Journal of Computer Applications. 185, 48 ( Dec 2023), 17-23. DOI=10.5120/ijca2023923309

@article{ 10.5120/ijca2023923309,
author = { Aris Prayogo, Enny Itje Sela },
title = { Implementation of Citra Technology to Identify the Freshness of Shrimp for Consumption },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2023 },
volume = { 185 },
number = { 48 },
month = { Dec },
year = { 2023 },
issn = { 0975-8887 },
pages = { 17-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number48/33014-2023923309/ },
doi = { 10.5120/ijca2023923309 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:29:07.502246+05:30
%A Aris Prayogo
%A Enny Itje Sela
%T Implementation of Citra Technology to Identify the Freshness of Shrimp for Consumption
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 48
%P 17-23
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The increasing market demand for shrimp makes many parties take advantage of this condition by selling shrimp that are not suitable for consumption such as rotten shrimp, diseased shrimp and formalin. To ensure the quality of shrimp received by consumers, it is necessary to test the freshness, so far the tests carried out through microbiological and chemical analysis but in this way it is less effective because it takes longer time, requires a lot of labor, requires a fairly expensive cost, so it affects the production of shrimp. The method used in this research is Convolutional Neural Network (CNN) which is done through classification with a preprocessing stage consisting of rescale, rotation range, horizontal flip, shear range, fill mode, width shift range, height shift range and zoom range. This classification stage produces shrimp freshness output which is divided into 3 categories. System development with the convolutional neural network method gets the best accuracy of 99.39% by using a learning rate of 0.001 and max epoch 100 with the results of the classification of the three classes with the tested Citra is correct..

References
  1. N. D. Singh, M. Krishnan, N. Sivaramane, R. V. and V. R. Kiresur, "Market integration and price transmission in Indian shrimp exports," Aquaculture, vol. 561, 2022.
  2. F. Zhan, Z. Li, D. Pan, S. Benjakul, X. Li and B. Zhang, "Investigating the migration hypothesis: Effects of trypsin-like protease on the quality of muscle proteins of red shrimp (Solenocera crassicornis) during cold storage," Food Chemistry: X, vol. 20, 2023.
  3. Y. Zhou, L. Jiao, J. Wu, Y. Zhang, Q. Zhu and D. Dong, "Non-destructive and in-situ detection of shrimp freshness using mid-infrared fiber-optic evanescent wave spectroscopy," Food Chemistry, vol. 422, 2023.
  4. S. B. Hashim and et. al, "Enhancement of a hybrid colorimetric film incorporating Origanum compactum essential oil as antibacterial and monitor chicken breast and shrimp freshness," Food Chemistry, vol. 432, 2024.
  5. E. I. Sela and A. Harjoko, "Deteksi Dan Identifikasi Ukuran Obyek Abnormal (Studi Kasus : Citra Otak Manusia)," Seminar Nasional Informatika (SEMNASIF), vol. 1, no. 1, 2011.
  6. K. Wang, C. Zhang, R. Wang and X. Ding, "Quality non-destructive diagnosis of red shrimp based on image processing," Journal of Food Engineering, vol. 357, 2023.
  7. A. Jahedsaravani, M. Massinaei and M. Zarie, "Measurement of bubble size and froth velocity using convolutional neural networks," Minerals Engineering, vol. 204, 2023.
  8. W. Chen and M. Li, "Standardized motion detection and real time heart rate monitoring of aerobics training based on convolution neural network," Preventive Medicine, vol. 174, 2023.
  9. E. Oluwasakin, "Minimization of high computational cost in data preprocessing and modeling using MPI4Py," Machine Learning with Applications, vol. 13, 2023.
  10. J. Mao, Y. Zhu, M. Chen, G. Chen, C. Yan and D. Liu, "A contradiction solving method for complex product conceptual design based on deep learning and technological evolution patterns," Advanced Engineering Informatics, vol. 55, 2023.
  11. A. E. Zimmermann, E. E. King and D. D. Bode, "Effectiveness and Utility of Flowcharts on Learning in a Classroom Setting: A Mixed-Methods Study," American Journal of Pharmaceutical Education, 2023.
  12. J. Bascunana, S. Leon, M. Gonzales-Miquel, E. J. Gonzalez and J. Ramirez, "Impact of Jupyter Notebook as a tool to enhance the learning process in chemical engineering modules," Education for Chemical Engineers, vol. 44, pp. 155-163, 2023.
  13. D. J. Clarke, "Appyters: Turning Jupyter Notebooks into data-driven web apps," Patterns, vol. 2, no. 3, 2021.
  14. A. J. Cerveira, "Automating behavioral analysis in neuroscience: Development of an open-source python software for more consistent and reliable results," Journal of Neuroscience Methods, vol. 398, 2023.
  15. P. Jalili, A. Shateri, A. M. Ganji, B. Jalili and D. D. Ganji, "Analytical analyzing mixed convection flow of nanofluid in a vertical channel using python approach," Result in Physics, vol. 52, 2023.
Index Terms

Computer Science
Information Sciences

Keywords

Convolutional Neural Network (CNN) Shrimp Freshness