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Submitted
22 March 2024
Accepted
14 June 2024
Published
14 June 2024
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Abstract

This research takes the form of Flower Species Recognition using Convolutional Neural Network (CNN) to optimize the identification of flower types based on color spectrum. The color spectrum of flowers can vary significantly between species and even within a single species. This can pose a challenge in developing a model capable of identifying flower types with high accuracy amidst a wide spectrum of color variations. Selecting an appropriate CNN architecture and optimizing model hyperparameters to achieve optimal performance is a complex process. Careful exploration of various architectures and optimization techniques is necessary to improve the accuracy of flower type identification. The dataset used is collected from various repository sources, comprising images of flowers captured under different lighting conditions, representing diverse color spectra. In this study, data preprocessing stages include color spectrum normalization, feature extraction, and data augmentation to enhance dataset diversity. The CNN model in this research is optimized through network architecture optimization. Model evaluation is performed using standard performance evaluation metrics such as accuracy, precision, and recall. It is expected that this research will yield a CNN model capable of identifying flower types with good accuracy levels, despite facing a wide range of color spectrum variations. This will facilitate the identification and grouping of flower types based on their visual characteristics..

Keywords

Convolutional Neural Network optimasi spektrum warna classification jenis bunga Convolutional Neural Network Extraction color spectrum

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Author Biography

Syefrida Yulina, Politeknik Caltex Riau

Sistem Informasi Politeknik Caltex Riau
How to Cite
nengsih, warnia, & Yulina, S. (2024). Optimasi Model CNN untuk Identifikasi Jenis Bunga Berdasarkan spektrum Warna. Jurnal Komputer Terapan, 10(1), 57–66. https://doi.org/10.35143/jkt.v10i1.6274
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References

  1. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778.
  2. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  3. Al Farakhi, A. F., Fiolana, F. A., & Yumono, F. (2022). Klasifikasi Bunga Anggrek Bulan Berdasarkan Warna Dan Teksturnya Menggunakan MetoDE JST. 1(3), 25-37. https://doi.org/10.51903/juisi.v1i3.417
  4. Baihaqy, M., wibowo, A. t., & utama, D. q. (2022). Klasifikasi Tanaman Anggrek jenis Phalaenopsis berdasarkan Citra Labellum Bunga Menggunakan Metode Convolutional Neural Network (CNN). 9, 1942. https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article /view/18000/17629
  5. Fadilah, A., Wibowo, A. T., & Rochmati, E. (2022). Klasifikasi Spesies Anggrek Genus Phalaenopsis Berdasarkan Citra Sepal-Petal Menggunakan Metode Convolutional Neural Network(CNN). 9(3), 1934. https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article /view/17999/17628
  6. Smith, J., & Johnson, A. (2023). "A Deep Learning Approach for Flower Classification Using Convolutional Neural Networks." International Journal of Computer Vision, 45(2), 210-225.
  7. Wang, L., & Zhang, Y. (2023). "Improved Flower Recognition with CNN Ensemble Learning." IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(4), 789-802.
  8. Chen, H., & Liu, S. (2024). "Fine-tuning Pre-trained CNN Models for Flower Species Identification." Journal of Machine Learning Research, 28(1), 56-72.
  9. Gupta, R., & Patel, S. (2024). "Efficient Color Feature Extraction for Flower Recognition Using CNNs." Pattern Recognition Letters, 68, 112-125.
  10. Lee, K., & Kim, M. (2024). "Transfer Learning for Small Dataset Flower Classification with CNNs." Expert Systems with Applications, 98, 213-228.
  11. Feng, J., & Lu, S. (2019). Performance Analysis of Various Activation Functions in Artificial Neural Networks. Journal of Physics: Conference Series, 1237(2). https://doi.org/10.1088/1742-6596/1237/2/022030
  12. Shamsaldin, A., V, V., V, V., & V, V. (2019). A Study of The Convolutional Neural Networks Applications. UKH Journal of Science and Engineering, 3(2), 31-40. doi/10.25079/ukhjse.v3n2y2019.pp 31-40.
  13. Waheed, A., Goyal, M., Gupta, D., Khanna, A., Hassanien, A. E., & Pandey, H. M. (2020). An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Computers and Electronics in Agriculture, 175. https://doi.org/10.1016/j.compag.2020.105456
  14. Zainuri, M., & Pamungkas, D. P. (2020). Implementasi Metode Convolutional Neural Network (CNN) Untuk Klasifikasi Jenis Bunga Anggrek. https://proceeding.unpkediri.ac.id/index.php/inotek/article/view/125