MEAT IMAGE CLASSIFICATION USING DEEP LEARNING WITH RESNET152V2 ARCHITECTURE

Authors

  • Taopik Hidayat Universitas Nusa Mandiri
  • Daniati Uki Eka Saputri Universitas Nusa Mandiri
  • Faruq Aziz Universitas Nusa Mandiri

DOI:

https://doi.org/10.33480/techno.v19i2.3932

Keywords:

Classification, ResNet152V2, Red Meat

Abstract

Meat is a food ingredient that can be consumed by humans and consists of essential nutrients, especially protein, which are needed for various physiological functions in the human body. Beef, goat and pork are meats that are commonly used by Indonesian people as daily processed foods. A very high level of meat consumption results in a high economic value of meat consumption. However, many people do not know how to distinguish between the types of beef, mutton and pork. This study aims to classify types of beef, goat and pork using the ResNet152V2 algorithm. The data used are 600 images with 200 images of beef, 200 images of mutton and 200 images of pork. The process carried out is pre-processing using 4 stages, namely image augmentation, image sharpness process, then the image is resized to adjust the size needed by the algorithm. The last pre-processing is to perform the image normalization process. After the pre-processing is done, then the data training stage is carried out using the ResNet152V2 algorithm to build a classification model and then the model is tested against data testing to get the results of the optimal classification of pork, goat and beef images by looking at the results of accuracy and loss values.

Downloads

Published

2022-09-30

How to Cite

Hidayat, T., Saputri, D. U. E., & Aziz, F. (2022). MEAT IMAGE CLASSIFICATION USING DEEP LEARNING WITH RESNET152V2 ARCHITECTURE. Jurnal Techno Nusa Mandiri, 19(2), 131–140. https://doi.org/10.33480/techno.v19i2.3932

Issue

Vol. 19 No. 2 (2022): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of September 2022

Section

Articles

License

The copyright of any article in the TECHNO Nusa Mandiri Journal is fully held by the author under the Creative Commons CC BY-NC license. The copyright in each article belongs to the author. Authors retain all their rights to published works, not limited to the rights set out on this page. The author acknowledges that Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) is the first to publish with a Creative Commons Attribution 4.0 International license (CC BY-NC). Authors can enter articles separately, manage non-exclusive distribution, from manuscripts that have been published in this journal into another version (for example: sent to author affiliation respository, publication into books, etc.), by acknowledging that the manuscript was published for the first time in Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri); The author guarantees that the original article, written by the stated author, has never been published before, does not contain any statements that violate the law, does not violate the rights of others, is subject to the copyright which is exclusively held by the author. If an article was prepared jointly by more than one author, each author submitting the manuscript warrants that he has been authorized by all co-authors to agree to copyright and license notices (agreements) on their behalf, and agrees to notify the co-authors of the terms of this policy. Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) will not be held responsible for anything that may have occurred due to the author's internal disputes.