Classification of meat using the convolutional neural network
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Classification of meat using the convolutional neural network |
| 2. | Creator | Author's name, affiliation, country | Detty Purnamasari; Universitas Gunadarma; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Koko Bachrudin; Universitas Gunadarma; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Dede Herman Suryana; Universitas Gunadarma; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Robert Robert; Universitas Gunadarma; Indonesia |
| 3. | Subject | Discipline(s) | Deep Learning |
| 3. | Subject | Keyword(s) | Classification; Convolutional neural network; Deep learning; Image processing; Meat |
| 4. | Description | Abstract | Every animal meat has different color and texture, for example, beef has a dark red color with a chewy texture, while pork has a pale red color and smooth fiber. A previous study has classified types of meat using gray level co-ocurrence matrix (GLCM), hue saturation value (HSV), and color intensity. In this research, we created meat classification between beef, pork, and horse meat using a convolutional neural network (CNN) develop in jupyter notebook, using the MobileNetV2 model, and 315 meat images as a dataset divided into 3 groups, 70% image for the training dataset, 20% image for the testing dataset, and 10% image for validation dataset. Before dividing the image into 3 groups, the image is resized to 224×224, and convert the color to grayscale. The model is trained with a training dataset, the epoch of 50, and Adam optimizer, the results show an accuracy of 93.15%. |
| 5. | Publisher | Organizing agency, location | Institute of Advanced Engineering and Science |
| 6. | Contributor | Sponsor(s) | Universitas Gunadarma; DGX UG Development Team |
| 7. | Date | (YYYY-MM-DD) | 2023-12-01 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ijai.iaescore.com/index.php/IJAI/article/view/22526 |
| 10. | Identifier | Digital Object Identifier (DOI) | http://doi.org/10.11591/ijai.v12.i4.pp1845-1853 |
| 11. | Source | Title; vol., no. (year) | IAES International Journal of Artificial Intelligence (IJ-AI); Vol 12, No 4: December 2023 |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2023 Institute of Advanced Engineering and Science This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |