Dimensional Reduction of Underwater Shrimp Digital Image Using the Principal Component Analysis Algorithm
Shrimps are aquaculture products highly needed by the people and this is the reason their growth needs to be monitored using underwater digital images. However, the large dimensions of the shrimp digital images usually make the processing difficult. Therefore, this research focuses on reducing the d...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/85/e3sconf_icenis2023_02061.pdf |
| _version_ | 1797345490902188032 |
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| author | Setiawan Arif Hadiyanto Hadiyanto Widodo Catur Edi |
| author_facet | Setiawan Arif Hadiyanto Hadiyanto Widodo Catur Edi |
| author_sort | Setiawan Arif |
| collection | DOAJ |
| description | Shrimps are aquaculture products highly needed by the people and this is the reason their growth needs to be monitored using underwater digital images. However, the large dimensions of the shrimp digital images usually make the processing difficult. Therefore, this research focuses on reducing the dimensions of underwater shrimp digital images without reducing their information through the application of the Principal Component Analysis (PCA) algorithm. This was achieved using 4 digital shrimp images extracted from video data with the number of columns 398 for each image. The results showed that 12 PCs were produced and this means the reduced digital images with new dimensions have 12 variable columns with data diversity distributed based on a total variance of 95.61%. Moreover, the original and reduced digital images were compared and the lowest value of MSE produced was 94.12, the minimum value of RMSE was 9.54, and the highest value of PSNR was 8.06 db, and they were obtained in the 4th digital image. The experiment was conducted using 3 devices which include I3, I7, and Google Colab processor computers and the fastest computational result was produced at 2.1 seconds by the Google Colab processor. This means the PCA algorithm is good for the reduction of digital image dimensions as indicated by the production of 12 PC as the new variable dimensions for the reduced underwater image of shrimps. |
| first_indexed | 2024-03-08T11:18:22Z |
| format | Article |
| id | doaj.art-5ec5e652678a4cc5b11a12acd6f0ea67 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-08T11:18:22Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-5ec5e652678a4cc5b11a12acd6f0ea672024-01-26T10:28:00ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014480206110.1051/e3sconf/202344802061e3sconf_icenis2023_02061Dimensional Reduction of Underwater Shrimp Digital Image Using the Principal Component Analysis AlgorithmSetiawan Arif0Hadiyanto Hadiyanto1Widodo Catur Edi2Doctoral Program of Information Systems, School of Postgraduate Studies, Diponegoro UniversityCenter of Biomass and Renewable Energy (CBIORE), Department of Chemical Engineering, Diponegoro UniversityDepartment of Physics, Faculty of Science and Mathematics, Diponegoro UniversityShrimps are aquaculture products highly needed by the people and this is the reason their growth needs to be monitored using underwater digital images. However, the large dimensions of the shrimp digital images usually make the processing difficult. Therefore, this research focuses on reducing the dimensions of underwater shrimp digital images without reducing their information through the application of the Principal Component Analysis (PCA) algorithm. This was achieved using 4 digital shrimp images extracted from video data with the number of columns 398 for each image. The results showed that 12 PCs were produced and this means the reduced digital images with new dimensions have 12 variable columns with data diversity distributed based on a total variance of 95.61%. Moreover, the original and reduced digital images were compared and the lowest value of MSE produced was 94.12, the minimum value of RMSE was 9.54, and the highest value of PSNR was 8.06 db, and they were obtained in the 4th digital image. The experiment was conducted using 3 devices which include I3, I7, and Google Colab processor computers and the fastest computational result was produced at 2.1 seconds by the Google Colab processor. This means the PCA algorithm is good for the reduction of digital image dimensions as indicated by the production of 12 PC as the new variable dimensions for the reduced underwater image of shrimps.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/85/e3sconf_icenis2023_02061.pdf |
| spellingShingle | Setiawan Arif Hadiyanto Hadiyanto Widodo Catur Edi Dimensional Reduction of Underwater Shrimp Digital Image Using the Principal Component Analysis Algorithm E3S Web of Conferences |
| title | Dimensional Reduction of Underwater Shrimp Digital Image Using the Principal Component Analysis Algorithm |
| title_full | Dimensional Reduction of Underwater Shrimp Digital Image Using the Principal Component Analysis Algorithm |
| title_fullStr | Dimensional Reduction of Underwater Shrimp Digital Image Using the Principal Component Analysis Algorithm |
| title_full_unstemmed | Dimensional Reduction of Underwater Shrimp Digital Image Using the Principal Component Analysis Algorithm |
| title_short | Dimensional Reduction of Underwater Shrimp Digital Image Using the Principal Component Analysis Algorithm |
| title_sort | dimensional reduction of underwater shrimp digital image using the principal component analysis algorithm |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/85/e3sconf_icenis2023_02061.pdf |
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