Klasifikasi Gambar Pemandangan dengan Kecerdasan Buatan Berbasis CNN
Article Sidebar
Main Article Content
Abstract
The use of Artificial Intelligence based on Convolutional Neural Network (CNN) has made remarkable advancements in visual analysis, particularly in landscape image classification. This study applies the CNN method to automatically classify landscape images. Through sophisticated network training and feature extraction steps, CNN can recognize unique patterns and features from various landscape categories, such as mountains, forests, streets, seas, and glaciers. The key advantage of CNN lies in its ability to identify complex and abstract features in images. The evaluation results show that the CNN model achieves satisfying accuracy in classifying landscape images. The application of this method offers practical benefits in various areas, including location recognition, virtual travel, and environmental analysis. AI based on CNN opens new possibilities in visual landscape recognition and its potential to contribute to automated understanding of the beauty of nature
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Qin, J., Pan, W., Xiang, X., Tan, Y., & Hou, G. (2020). A biological image classification method based on improved CNN. Ecological Informatics, 58, 101093. DOI: https://doi.org/10.1016/j.ecoinf.2020.10109.
Azmi, K., Defit, S., & Sumijan, S. (2023). Implementasi convolutional neural network (CNN) untuk klasifikasi batik tanah liat sumatera barat. Jurnal Unitek, 16(1), 28-40.
Xin, R., Zhang, J., & Shao, Y. (2020). Complex network classification with convolutional neural network. Tsinghua Science and technology, 25(4), 447-457.
Zhang, P., Wang, X., Zhang, W., & Chen, J. (2018). Learning spatial–spectral–temporal EEG features with recurrent 3D convolutional neural networks for cross-task mental workload assessment. IEEE Transactions on neural systems and rehabilitation engineering, 27(1), 31-42. DOI: 10.1109/TNSRE.2018.2884641.
Lu, Y., Lu, G., Lin, R., Li, J., & Zhang, D. (2019). SRGC-Nets: Sparse repeated group convolutional neural networks. IEEE transactions on neural networks and learning systems, 31(8), 2889-2902. DOI: 10.1109/TNNLS.2019.2933665.
Lin, B. S., Cheng, J. S., Liao, H. C., Yang, L. W., Yang, T., & Chen, K. C. (2021). Improvement of multi-lines bridge defect classification by hierarchical architecture in artificial intelligence automatic defect classification. IEEE Transactions on Semiconductor Manufacturing, 34(3), 346-351. DOI: 10.1109/TSM.2021.3076808.
Qaffas, A. A., Hajkacem, M. B., Ncir, C. B., & Nasraoui, O. (2023). Interpretable Multi-criteria ABC analysis based on semi-supervised clustering and Explainable Artificial Intelligence. IEEE Access. DOI: 10.1109/ACCESS.2023.3272403.
Yuan, L., Wei, X., Shen, H., Zeng, L. L., & Hu, D. (2018). Multi-center brain imaging classification using a novel 3D CNN approach. IEEE Access, 6, 49925-49934. DOI: 10.1109/ACCESS.2018.2868813.