Classification of Fish Species with Image Data Using K-Nearest Neighbor
Abstract
Abstract— Classification is a technique that many of us encounter in everyday life, classification science is also growing and being applied to various types of data and cases in everyday life, in computer science classification has been developed to facilitate human work, one example of its application is to classify fish species in the world, the number of fish species in the world is very much so that there are still many people who are sometimes confused to distinguish them, therefore in this study a study will be conducted to classify fish species using the K-Nearest Neighbor Method. 4 types of fish, all data totaling 160 data. The purpose of this study was to test the K-Nearest Neighbor method for classifying fish species based on color, texture, and shape features. Based on the test results, the accuracy value of the truth is obtained using the value of K = 7 with a percentage of the truth of 77.50%, the second-highest accuracy value is the value of K = 10, namely 76.88%. Based on the results of this study, it can be concluded that the K-Nearest Neighbor method has a good enough ability to classify, but it can be done by adding variables or adding more amount of data, and using other types of fish.
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Arifin, Muhammad Zainul, dkk (2019) Modul Massa Daratan dan Lautan http://www.pusdik.kkp.go.id/
Fautin D, Dalton P, Incze LS, Leong J-AC, Pautzke C, Rosenberg A, et al. An Overview of Marine Biodiversity in United States Waters. PloS one.2010;5(8): e11914. doi: 10.1371/journal.pone.0011914
Xu L, Wang X, Van Damme K, Huang D, Li Y, Wang L, et al. Assessment of fish diversity in the South China Sea using DNA taxonomy. Fisheries Research. 2021; 233:105771. doi: 10.1016/j.fishres.2020.105771.
Thu PT, Huang WC, Chou TK, Van NQ, Liao TY. DNA barcoding of coastal ray-finned fishes in Vietnam. PloS one. 2019;14(9):e0222631. doi: 10.1371/journal.pone.0222631.
Hebert PD, Cywinska A, Ball SL, deWaard JR. Biological identifications through DNA barcodes. Proceedings Biological sciences. 2003;270(1512):313-21. doi: 10.1098/rspb.2002.2218. PubMed PMID: 12614582; PubMed Central PMCID: PMC1691236.
Ramirez JL, Rosas-Puchuri U, Canedo RM, Alfaro-Shigueto J, Ayon P, Zelada-Mazmela E, et al. DNA barcoding in the Southeast Pacific marine realm: Lowcoverage and geographic representation despite high diversity. PloS one. 2020;15(12): e0244323. doi: 10.1371/journal.pone.0244323. PubMed PMID: 33370342; PubMed Central PMCID: PMC7769448.
Liang H, Meng Y, Luo X, Li Z, Zou G. Species identification of DNA barcoding based on COI gene sequences in Bagridae catfishes. Journal of Fishery Sciences of China. 2018;25(4):772. doi: 10.3724/sp.j.1118.2018.18036.
Xu L, Van Damme K, Li H, Ji Y, Wang X, Du F. A molecular approach to the identification of marine fish of the Dongsha Islands (South China Sea). Fisheries Research. 2019; 213:105-12. doi: 10.1016/j.fishres.2019.01.011
Ren BQ, Xiang XG, Chen ZD. Species identification of Alnus (Betulaceae) using nrDNA and cpDNA genetic markers. Mol Ecol Resour. 2010;10(4):594-605. doi: 10.1111/j.1755-0998.2009.02815. x. PubMed PMID: 21565064.
T. F. Efendi, R. Rahmadi, and Y. Prayudi, “Rancang Bangun Sistem Untuk Manajemen Barang Bukti Fisik dan Chain of Custody (CoC) pada Penyimpananan Laboratorium Forensika Digital,” J. Teknol. dan Manaj. Inform., vol. 6, no. 2, pp. 53–63, 2020, doi: 10.26905/jtmi.v6i2.4177.
Kaharudin, Kusrini, Wati Vera, dkk. (2019) Classification of Spice Types UsingK-Nearest Neighbor Algorithm. International Conference on Information and Communications Technology. Doi: 10.1109/ICOIACT46704.2019.8938515
Andayani U, Wijaya Alex, dkk. Fish Species Classification Using Probabilistic Neural Network, Journal of Physics: Conference Series, The 3rd International Conference on Computing and Applied Informatics 2018 IOP Conf. Series: Journal of Physics: Conf. Series 1235 (2019) 012094 IOP Publishing doi.org/10.1088/1742-6596/1235/1/012094
T. F. Efendi and M. Sidiq, “Analysis of Sales System Implementation in Primary Koperasi Tribuana II,” vol. 01, no. 03, pp. 3–6, 2020.
Montablo Francis Jesmar P, Hernandez Alexander A. Classification of Fish Species with Augmented Data using Deep Convolutional Neural Network. 2019 IEEE 9th International Conference on System Engineering and Technology (ICSET), 7 October 2019, Shah Alam, Malaysia
Alsmadi Mutasem K, Almarashdeh Ibrahim. A survey on fish classification techniques. Journal of King Saud University – Computer and Information Sciences. doi.org/10.1016/j.jksuci.2020.07.005
Jin Lina, Yu Jiong, dkk. A deep learning model for fish classification base on DNA barcode. Doi.org/10.1101/2021/02/15/431244
F. Efendi and M. Krisanty, “Warehouse Data System Analysis PT . Kanaan Global Indonesia,” vol. 01, no. 02, pp. 2–5, 2020.
Adebayo Daramola S, Olumide Omololu. Fish Classification Algorithm using Single Value Decomposition. Internation Journal of Innovative Research in Science, Engineering, and Technology. Vol.5, Issue 2, February 2016.
O. Ulucan, D. Karakaya, and M. Turkan. (2020) A large-scale dataset for fish segmentation and classification.
In Conf. Innovations Intell. Syst. Appli. (ASYU).
Muslihah, I., Muqorobin, M., Rokhmah, S., & Rais, N. A. R. (2020). Texture Characteristic of Local Binary Pattern on Face Recognition with PROBABILISTIC LINEAR DISCRIMINANT ANALYSIS. International Journal of Computer and Information System (IJCIS), 1(1).
Muqorobin, M., Rokhmah, S., Muslihah, I., & Rais, N. A. R. (2020). Classification of Community Complaints Against Public Services on Twitter. International Journal of Computer and Information System (IJCIS), 1(1).
Muqorobin, M., Kusrini, K., Rokhmah, S., & Muslihah, I. (2020). Estimation System For Late Payment Of School Tuition Fees. International Journal of Computer and Information System, 1(1), 341475.
DOI: https://doi.org/10.29040/ijcis.v2i2.33
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