# SE-ResNet: An Attention Enhanced CNN for Classification of Indonesian Medicinal Plants
> Rahmat M.A.

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_105034218756
Jurnal / Konferensi: Proceedings of the International Conference on Information Technology and Electrical Engineering Icitee
Tahun terbit: 2025
DOI: https://doi.org/10.1109/ICITEE66631.2025.11338386
ISSN: 27660419
Citations: 0

## Authors
- Rahmat M.A.

## Abstract
Correctly identifying medicinal plants is very important for protecting biodiversity, supporting pharmaceutical research, and keeping traditional knowledge alive, especially in areas with a lot of biodiversity like Indonesia. This study suggests a new version of the SE-ResNet-50 architecture for classifying Indonesian medicinal plants by their leaf images. The model uses a pretrained ResNet-50 on ImageNet to extract hierarchical features. A Squeeze and Excitation (SE) block is added at the last convolutional layer to make high level discriminative features stand out more. We used a set of $\mathbf{1, 5 0 0}$ pictures of plants in ten different categories to train and test the model. The experimental results show that the proposed model has a classification accuracy of 97.65%, which is much better than a baseline ResNet- 50 model without pretrained weights, which only got 89.94%. These results show that using residual learning, attention mechanisms, and transfer learning together works well for very detailed plant classification tasks.

## Keywords
- Discriminative model
- Artificial intelligence
- Computer science
- Block (permutation group theory)
- Indonesian
- Medicinal plants
- Set (abstract data type)
- Machine learning
- Baseline (sea)
- Training set
- Transfer of learning
- Scheme (mathematics)
- Pattern recognition (psychology)
- Test set
- Key (lock)
- Layer (electronics)
- Feature (linguistics)
- Residual
- Supervised learning
- Component (thermodynamics)
- Feature extraction

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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