Betta Fish Identification System Based On Convolutional Neural Network
Abstract
This study developed an automated identification system based on the Convolutional Neural Network (CNN) to classify Betta Splendens, a fish species with high economic value in Indonesia. The system aims to improve accuracy and efficiency in the identification process. The research was divided into several experiments, where the data was split into 320 images for training, 80 for validation, and 100 for testing. We used two optimizers, Adam and RMSprop. The Adam optimizer experiments conducted two stages with learning rates of 0.0001 and 0.001, each with 100 and 200 epochs. The results showed that a lower learning rate (0.0001) with 200 epochs yielded the best test accuracy of 71%, while a learning rate of 0.001 caused accuracy to stagnate at 66%, indicating potential overfitting. The RMSprop optimizer with a learning rate of 0.00001 demonstrated good stability, though with slightly lower accuracy than Adam. This study highlights the importance of selecting the appropriate learning rate and number of epochs to achieve an optimal balance between training, validation, and testing accuracy, ensuring the model generalizes well to new data.
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References
M. T. A. Syech Ahmad and B. Sugiarto, “Implementasi Convolutional Neural Network (CNN) Untuk Klasifikasi Ikan Cupang Berbasis Mobile,” Digital Transformation Technology, vol. 3, no. 2, pp. 712–723, Dec. 2023, doi: 10.47709/digitech.v3i2.3245.
W. D. Setyawan, A. Nilogiri, and Q. A’yun, “Implementasi Convolution Neural Network (CNN) Untuk Klasifikasi Pada Citra Ikan Cupang Hias,” JTIK (Jurnal Teknik Informatika Kaputama), vol. 7, no. 1, 2023, doi: 10.59697/jtik.v7i1.45.
Y. Tian, “Artificial Intelligence Image Recognition Method Based On Convolutional Neural Network Algorithm,” IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.3006097.
U. Sri Rahmadhani and N. Lysbetti Marpaung, “Klasifikasi Jamur Berdasarkan Genus Dengan Menggunakan Metode CNN,” Jurnal Informatika: Jurnal pengembangan IT, vol. 8, no. 2, pp. 169–173, 2023.
I. Ariawan, W. Aprizal Arifin, A. Armelita Rosalia, and N. Tufailah, “Klasifikasi Tiga Genus Ikan Karang Menggunakan Convolution Neural Network,” Jurnal Ilmu dan Teknologi Kelautan Tropis, vol. 14, no. 2, pp. 205–216, 2024, doi: 10.29244/jitkt.v14i1.33633.
I. Anugrah, A. Cendekia Siregar, and B. C. Octariadi, “Perbandingan Model Arsitektur Cnn Dengan Metode Transfer Learning Untuk Klasifikasi Spesies Ikan Laut,” Progresif: Jurnal Ilmiah Komputer, vol. 20, no. 1, pp. 444–453, 2024, doi: 10.35889/progresif.v20i1.1834.
A. Azis, “Identifikasi Jenis Ikan Menggunakan Model Hybrid Deep Learning Dan Algoritma Klasifikasi,” SEBATIK, pp. 201–206, 2020, Accessed: Aug. 23, 2024. [Online]. Available: https://jurnal.wicida.ac.id/index.php/sebatik/article/view/1057
N. Abdurrahman, B. Rahmat, and A. N. Sihananto, “Perbandingan Performa Klasifikasi Citra Ikan Menggunakan Metode K-Nearest Neighbor (K-NN) Dan Convolutional Neural Network (CNN),” Jurnal Sistem Informasi dan Informatika (JUSIFOR), vol. 2, no. 2, pp. 84–93, Dec. 2023, doi: 10.33379/jusifor.v2i2.3728.
G. Papastergiou, V. C. Gerogiannis, A. Xenakis, G. Papastergiou, and G. Stamoulis, “Applying A Convolutional Neural Network In An Iot Robotic System For Plant Disease Diagnosis,” 2023. [Online]. Available: https://www.researchgate.net/publication/343386581
A. M. Ismael and A. Şengür, “Deep Learning Approaches For COVID-19 Detection Based On Chest X-Ray Images,” Expert Syst Appl, vol. 164, p. 114054, Feb. 2021, doi: 10.1016/j.eswa.2020.114054.
A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, “A Survey Of The Recent Architectures Of Deep Convolutional Neural Networks,” Artif Intell Rev, vol. 53, no. 8, 2020, doi: 10.1007/s10462-020-09825-6.
F. A. Mohammed, K. K. Tune, B. G. Assefa, M. Jett, and S. Muhie, “Medical Image Classifications Using Convolutional Neural Networks: A Survey Of Current Methods And Statistical Modeling Of The Literature,” Mar. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/make6010033.
A. Saber, M. Sakr, O. M. Abo-Seida, A. Keshk, and H. Chen, “A Novel Deep-Learning Model For Automatic Detection And Classification Of Breast Cancer Using The Transfer-Learning Technique,” IEEE Access, vol. 9, 2021, doi: 10.1109/ACCESS.2021.3079204.
D. R. Sarvamangala and R. V. Kulkarni, “Convolutional Neural Networks In Medical Image Understanding: A Survey,” Evol Intell, vol. 15, no. 1, pp. 1–22, Mar. 2022, doi: 10.1007/s12065-020-00540-3.
L. Alzubaidi et al., “Review Of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions,” J Big Data, vol. 8, no. 1, 2021, doi: 10.1186/s40537-021-00444-8.
M. Reyad, A. M. Sarhan, and M. Arafa, “A Modified Adam Algorithm For Deep Neural Network Optimization,” Neural Comput Appl, vol. 35, no. 23, 2023, doi: 10.1007/s00521-023-08568-z.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual Explanations From Deep Networks Via Gradient-Based Localization,” Int J Comput Vis, vol. 128, no. 2, 2020, doi: 10.1007/s11263-019-01228-7.
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