MobileNetV2-D and multiple cameras for swiftlet nest classification based on feather intensity
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | MobileNetV2-D and multiple cameras for swiftlet nest classification based on feather intensity |
| 2. | Creator | Author's name, affiliation, country | Denny Indrajaya; Satya Wacana Christian University; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Hanna Arini Parhusip; Satya Wacana Christian University; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Suryasatriya Trihandaru; Satya Wacana Christian University; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Djoko Hartanto; PT Waleta Asia Jaya; Indonesia |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Image classification; MobileNetV2-D; Multiple cameras; Sorting machine; Swiftlet nest |
| 4. | Description | Abstract | MobileNetV2-D is a modified version of MobileNetV2, which is the novelty of this article. The algorithm is used to classify swiftlet nests into seven classes. In 2023, PT Waleta Asia Jaya is required to achieve a 7-fold increase in the export quota of swiftlet nests. To meet the quota, the company made a machine that can recognize swiftlet nest objects, which are classified into seven classes based on feather intensity, namely BRS, BR, BST, BS, BBT, BB, and BB2 for the light feathers to the heavy feathers, respectively. The input image is a combination of four images from four cameras with different positions, which adds to the novelty of MobileNetV2-D for the particular problem here. From the evaluation that has been carried out, the accuracy value of the MobileNetV2-D model was better than the MobileNetV2 model, i.e., the accuracy value of the MobileNetV2-D model was 99.9928% for the training dataset and 94.0723% for the testing dataset. Moreover, the speed of MobileNetV2-D is better than MobileNetV2- architecture. |
| 5. | Publisher | Organizing agency, location | Institute of Advanced Engineering and Science |
| 6. | Contributor | Sponsor(s) | Satya Wacana Christian University; PT Waleta Asia Jaya |
| 7. | Date | (YYYY-MM-DD) | 2024-05-01 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ijeecs.iaescore.com/index.php/IJEECS/article/view/36030 |
| 10. | Identifier | Digital Object Identifier (DOI) | http://doi.org/10.11591/ijeecs.v34.i2.pp1144-1158 |
| 11. | Source | Title; vol., no. (year) | Indonesian Journal of Electrical Engineering and Computer Science; Vol 34, No 2: May 2024 |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2024 Institute of Advanced Engineering and Science This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |