Penerapan Algoritma Yolov4-Tiny Dan Efficientnetv2-S Untuk Deteksi Kesegaran Ikan Gurami

Hans Richard Alim Natadjaja(1*), Daniel Martomanggolo Wonohadidjojo(2),

(1) Universitas Ciputra, Indonesia
(2) Universitas Ciputra, Indonesia
(*) Corresponding Author


Fish production is one of the largest in Indonesia. Among many fishery products, Gurami fish is one of the most widely processed by society. However, the decrease in the freshness of fishery products is susceptible to occur when they reach the hands of consumers. Several methods for detecting fish freshness have been applied to assist in obtaining fresh fish products. However, some of these methods have a lack of accuracy rate, not all of them were developed to detect gurami freshness. Therefore, a solution is needed to help people detect the freshness of Gurami fish and make it accessible on mobile devices. This solution can be realized by using Deep Learning methods. The methods proposed in this study use the Convolutional Neural Network (CNN) by utilizing the YOLOv4-Tiny algorithm to detect the Region of Interest (ROI) and the EfficientNetV2-S architecture freshness classification on Gurami fish images. The training process using both methods can produce an ROI detection model with a mean average precision of 93,58% and a classification model with an accuracy rate of 93%

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Kementerian Kelautan dan Perikanan, “Produksi Perikanan,” 2021. (accessed May 11, 2023).

E. Suprayitno, “Kajian Kesegaran Ikan Di Pasar Tradisional Dan Modern Kota Malang,” Journal of Fisheries and Marine Research, vol. 4, no. 2, pp. 289–295, Jul. 2020, doi:

S. Mitra, Most. N. Khatun, Md. M. H. Prodhan, and Md. A. Khan, “Consumer preference, willingness to pay, and market price of capture and culture fish: Do their attributes matter?,” Aquaculture, vol. 544, p. 737139, Nov. 2021, doi: 10.1016/j.aquaculture.2021.737139.

Badan Standardisasi Nasional, Ikan Segar. Jakarta, 2013. Accessed: May 28, 2023. [Online]. Available:

J. Chai, H. Zeng, A. Li, and E. W. T. Ngai, “Deep learning in computer vision: A critical review of emerging techniques and application scenarios,” Machine Learning with Applications, vol. 6, p. 100134, Dec. 2021, doi: 10.1016/j.mlwa.2021.100134.

M. Christiawan, L. Willyanto Santoso, and D. Haryadi Setiabudi, “Deteksi Tingkat Kesegaran Ikan Menggunakan Metode Convolutional Neural Network Dengan Parameter Mata dan Warna Insang,” Surabaya, 2020.

P. Jiang, D. Ergu, F. Liu, Y. Cai, and B. Ma, “A Review of Yolo Algorithm Developments,” Procedia Comput Sci, vol. 199, pp. 1066–1073, 2022, doi: 10.1016/j.procs.2022.01.135.

E. Prasetyo, N. Suciati, and C. Fatichah, “Yolov4-tiny with wing convolution layer for detecting fish body part,” Comput Electron Agric, vol. 198, p. 107023, Jul. 2022, doi: 10.1016/j.compag.2022.107023.

E. Prasetyo, R. Purbaningtyas, R. D. Adityo, N. Suciati, and C. Fatichah, “Combining MobileNetV1 and Depthwise Separable convolution bottleneck with Expansion for classifying the freshness of fish eyes,” Information Processing in Agriculture, vol. 9, no. 4, pp. 485–496, Dec. 2022, doi: 10.1016/j.inpa.2022.01.002.

M. Tan and Q. v. Le, “EfficientNetV2: Smaller Models and Faster Training,” Apr. 2021, Accessed: Jan. 16, 2023. [Online]. Available:

APJJI, “Profil Internet Indonesia 2022,” Asosiasi Penyelenggara Jasa Internet Indonesia (APJJI), no. June, 2022, Accessed: Jan. 22, 2023. [Online]. Available:

A. Hosna, E. Merry, J. Gyalmo, Z. Alom, Z. Aung, and M. A. Azim, “Transfer learning: a friendly introduction,” J Big Data, vol. 9, no. 1, p. 102, Oct. 2022, doi: 10.1186/s40537-022-00652-w.

C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “Scaled-YOLOv4: Scaling Cross Stage Partial Network,” Dec. 2020, Accessed: Jan. 22, 2023. [Online]. Available:

Keras, “Keras API Reference,” 2022. (accessed May 27, 2023).

M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The Pascal Visual Object Classes (VOC) Challenge,” Int J Comput Vis, vol. 88, no. 2, pp. 303–338, Jun. 2010, doi: 10.1007/s11263-009-0275-4.

M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The Pascal Visual Object Classes Challenge: A Retrospective,” Int J Comput Vis, vol. 111, no. 1, pp. 98–136, Jan. 2015, doi: 10.1007/s11263-014-0733-5.




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