OPTIMALISASI ARSITEKTUR DEEP-LEARNING UNTUK OTOMATISASI KLASIFIKASI IDENTIFIKASI SPESIES IKAN
Abstract
Penelitian ini membahas tentang optimalisasi arsitektur pada deep Learning untuk otomatisasi klasifikasi ikan melalui citra dengan berbagai macam latar belakang dan kondisi cahaya yang beragam. Beberapa riset terdahulu tentang klasifikasi spesies ikan telah dilakukan oleh beberapa peneliti di dunia menggunakan berbagai metode, termasuk Naïve bayes, CNN, dan jaringan deep learning. Dalam penelitian ini, akan dibandingkan tiga arsitektur deep learning (ResNet101v2, CoAtNet-0, dan EfficientNetV2B0) dengan tiga algoritma optimasi (Adam, SGD, dan MSProp) untuk mengetahui arsitektur yang terbaik untuk model deep learning pada otomatisasi identifikasi spesies ikan, yang terdiri dari 3.248 citra yang terbagi menjadi delapan kelas spesies, hasil dari pengujian model didapat bahwa ResNet101v2 yang dioptimalisasi oleh Adam memiliki nilai akurasi paling tinggi dibanding 2 Arsitektur lainya yaitu sebesar 62% .
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A. N.S., S. D., and R. K. S., “Naive Bayesian fusion based deep learning networks for multisegmented classification of fishes in aquaculture industries,” Ecol Inform, vol. 61, Mar. 2021, doi: 10.1016/j.ecoinf.2021.101248.
J. Soom, V. Pattanaik, M. Leier, and J. A. Tuhtan, “Environmentally adaptive fish or no-fish classification for river video fish counters using high-performance desktop and embedded hardware,” Ecol Inform, vol. 72, Dec. 2022, doi: 10.1016/j.ecoinf.2022.101817.
E. Prasetyo, N. Suciati, and C. Fatichah, “Multi-level residual network VGGNet for fish species classification,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 8, pp. 5286–5295, Sep. 2022, doi: 10.1016/j.jksuci.2021.05.015.
A. Banerjee et al., “Carp-DCAE: Deep convolutional autoencoder for carp fish classification,” Comput Electron Agric, vol. v. 196, pp. 106810--2022 v.196, 2022, doi: 10.1016/j.compag.2022.106810.
V. Kandimalla, M. Richard, F. Smith, J. Quirion, L. Torgo, and C. Whidden, “Automated Detection, Classification and Counting of Fish in Fish Passages With Deep Learning,” Front Mar Sci, vol. 8, Jan. 2022, doi: 10.3389/fmars.2021.823173.
S. Z. H. Shah et al., “Fish-Pak: Fish species dataset from Pakistan for visual features based classification,” Data Brief, vol. 27, Dec. 2019, doi: 10.1016/j.dib.2019.104565.
N. H. Shabrina, R. A. Lika, and S. Indarti, “Deep learning models for automatic identification of plant-parasitic nematode,” Artificial Intelligence in Agriculture, vol. 7, pp. 1–12, Mar. 2023, doi: 10.1016/j.aiia.2022.12.002.
M. De Graeve, N. Birse, Y. Hong, C. T. Elliott, L. Y. Hemeryck, and L. Vanhaecke, “Multivariate versus machine learning-based classification of rapid evaporative Ionisation mass spectrometry spectra towards industry based large-scale fish speciation,” Food Chem, vol. 404, Mar. 2023, doi: 10.1016/j.foodchem.2022.134632.
L. Boussaad and A. Boucetta, “An effective component-based age-invariant face recognition using Discriminant Correlation Analysis,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 5, pp. 1739–1747, May 2022, doi: 10.1016/j.jksuci.2020.08.009.
V. Kandimalla, M. Richard, F. Smith, J. Quirion, L. Torgo, and C. Whidden, “Automated Detection, Classification and Counting of Fish in Fish Passages With Deep Learning,” Front Mar Sci, vol. 8, Jan. 2022, doi: 10.3389/fmars.2021.823173.
Paper Whitecode, “Image Classification on ImageNet,” https://paperswithcode.com/sota/image-classification-on-imagenet, Apr. 06, 2023.
M. Yang, X. Huang, L. Huang, and G. Cai, “Diagnosis of Parkinson’s disease based on 3D ResNet: The frontal lobe is crucial,” Biomed Signal Process Control, vol. 85, p. 104904, Aug. 2023, doi: 10.1016/j.bspc.2023.104904.
B. Falahkhi, E. F. Achmal, M. Rizaldi, R. Rizki, and N. Yudistira, “Perbandingan Model AlexNet dan ResNet dalam Klasifikasi Citra Bunga Memanfaatkan Transfer Learning Comparison of AlexNet and ResNet Models in Flower Image Classification Utilizing Transfer Learning”, [Online]. Available: http://journal.ipb.ac.id/index.php/jika
Z. Dai, H. Liu, Q. V Le, and M. Tan, “CoAtNet: Marrying Convolution and Attention for All Data Sizes.”
M. Tan and V. Q. Le, “EfficientNetV2: Smaller Models and Faster Training,” Jun. 2021.
M. To?açar, B. Ergen, and Z. Cömert, “Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks,” Med Biol Eng Comput, vol. 59, no. 1, pp. 57–70, Jan. 2021, doi: 10.1007/s11517-020-02290-x.
DOI: http://dx.doi.org/10.20527/klik.v10i2.629
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