Klasifikasi Tingkat Kesegaran Ikan Nila Menggunakan K-Nearest Neighbor Berdasarkan Fitur Statistis Piksel Citra Mata Ikan

Authors

  • Rahmat Widadi Institut Teknologi Telkom Purwokerto, Purwokerto
  • Bongga Arifwidodo Institut Teknologi Telkom Purwokerto, Purwokerto
  • Kholidiyah Masykuroh Institut Teknologi Telkom Purwokerto, Purwokerto
  • Ariyatno Saputra Institut Teknologi Telkom Purwokerto, Purwokerto

DOI:

https://doi.org/10.30865/mib.v7i1.5196

Keywords:

Tilapia, K-Nearest Neighbor (KNN), Mean, Standard Deviation, Kurtosis, Skewness

Abstract

Healthy tilapia will experience a decrease in quality when stored out of water even with the refrigerator. The decline in fish quality can be seen from the fish's eye. In this study the aim was to develop a tilapia freshness classification system based on fisheye images utilizing image processing and machine learning. Fisheye images were taken at intervals of 0 to 16 hours after being removed from the water. Ten tilapias were used in this study. The distance between the camera and the fish has also been changed with a variation of 4 distances. The total data obtained is 640 fisheye images. The method used in fisheye image classification consists of two stages, namely feature extraction and classification. The four types of statistical features used from image pixel values are the mean, standard deviation, skewness, and kurtosis. While at the classification stage using K-Nearest Neighbor. The scenario that has been determined is then used at the system implementation stage using the Python Programming Language. Testing and analysis using k-fold cross validation and confusion matrix. In the results of the study, the accuracy rate was obtained with an average of 100% using 2 classes, namely 0-2 hours and 2-4 hours. The accuracy rate using 4 classes was obtained with an average of 75%, namely classes 0-2 hours, 2-4 hours, 10-12 hours, and 14-16 hours, and when using all classes, an average accuracy rate of 45% is obtained.

References

Food and Agriculture Organization of the United Nation, The State of World Fisheries and Aquaculture 2020. FAO, 2020. doi: 10.4060/ca9229en.

Kementerian Kelautan dan Perikanan Republik Indonesia, “Produksi Ikan Dengan Perbandingan Jenis Ikan,†2018. https://statistik.kkp.go.id/home.php?m=total_ikan&i=2#panel-footer

P. C. Hubbard, “Pheromones, Fish,†in Encyclopedia of Reproduction, Elsevier, 2018, pp. 458–464. doi: 10.1016/B978-0-12-809633-8.20592-X.

T. O. Magbanua and J. A. Ragaza, “Selected dietary plant-based proteins for growth and health response of Nile tilapia Oreochromis niloticus,†Aquac. Fish., p. S2468550X22000703, Apr. 2022, doi: 10.1016/j.aaf.2022.04.001.

F. Jim, P. Garamumhango, and C. Musara, “Comparative Analysis of Nutritional Value of Oreochromis niloticus (Linnaeus), Nile Tilapia, Meat from Three Different Ecosystems,†J. Food Qual., vol. 2017, pp. 1–8, 2017, doi: 10.1155/2017/6714347.

D. I. Ariestya, F. Swastawati, and E. Susanto, “Antimicrobial Activity of Microencapsulation Liquid Smoke on Tilapia [Oreochromis Niloticus (Linnaeus, 1758)] Meat for Preservatives in Cold Storage (± 5 C°),†Aquat. Procedia, vol. 7, pp. 19–27, Aug. 2016, doi: 10.1016/j.aqpro.2016.07.003.

T. Hidayat, “QUALITY ASSURANCE OF TILAPIA FISH (Oreochromis niloticus) FRESHNESS WITH TREATMENT OF WEEDING,†Food Sci. J., vol. 2, no. 2, p. 87, Dec. 2020, doi: 10.33512/fsj.v2i2.10139.

J.-W. Choi et al., “Novel application of an optical inspection system to determine the freshness of Scomber japonicus (mackerel) stored at a low temperature,†Food Sci. Biotechnol., vol. 29, no. 1, pp. 103–107, Jan. 2020, doi: 10.1007/s10068-019-00639-z.

M. M. M. Fouad, H. M. Zawbaa, N. El-Bendary, and A. E. Hassanien, “Automatic Nile Tilapia fish classification approach using machine learning techniques,†in 13th International Conference on Hybrid Intelligent Systems (HIS 2013), Gammarth, Tunisia, Dec. 2013, pp. 173–178. doi: 10.1109/HIS.2013.6920477.

R. M. Hernandez and A. A. Hernandez, “Classification of Nile Tilapia using Convolutional Neural Network,†in 2019 IEEE 9th International Conference on System Engineering and Technology (ICSET), Shah Alam, Malaysia, Oct. 2019, pp. 126–131. doi: 10.1109/ICSEngT.2019.8906453.

M. A. Rayan, A. Rahim, M. A. Rahman, Md. A. Marjan, and U. A. Md. E. Ali, “Fish Freshness Classification Using Combined Deep Learning Model,†in 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI), Rajshahi, Bangladesh, Jul. 2021, pp. 1–5. doi: 10.1109/ACMI53878.2021.9528138.

S. M. Gandhi and B. C. Sarkar, “Conventional and Statistical Resource/Reserve Estimation,†in Essentials of Mineral Exploration and Evaluation, Elsevier, 2016, pp. 271–288. doi: 10.1016/B978-0-12-805329-4.00018-1.

A. P. King and R. J. Eckersley, “Descriptive Statistics I: Univariate Statistics,†in Statistics for Biomedical Engineers and Scientists, Elsevier, 2019, pp. 1–21. doi: 10.1016/B978-0-08-102939-8.00010-4.

J. Lazar, J. H. Feng, and H. Hochheiser, “Statistical analysis,†in Research Methods in Human Computer Interaction, Elsevier, 2017, pp. 71–104. doi: 10.1016/B978-0-12-805390-4.00004-2.

X. Wang, Y. Liu, B. Xu, L. Li, and J. Xue, “A statistical feature based approach to distinguish PRCG from photographs,†Comput. Vis. Image Underst., vol. 128, pp. 84–93, Nov. 2014, doi: 10.1016/j.cviu.2014.07.007.

D. Chanal, N. Yousfi Steiner, R. Petrone, D. Chamagne, and M.-C. Péra, “Online Diagnosis of PEM Fuel Cell by Fuzzy C-Means Clustering,†in Encyclopedia of Energy Storage, Elsevier, 2022, pp. 359–393. doi: 10.1016/B978-0-12-819723-3.00099-8.

C. Mahanty and B. K. Mishra, “Medical data analysis in eHealth care for industry perspectives: applications,†in An Industrial IoT Approach for Pharmaceutical Industry Growth, Elsevier, 2020, pp. 305–335. doi: 10.1016/B978-0-12-821326-1.00013-9.

Ö. F. Ertuğrul and M. E. Tağluk, “A novel version of k nearest neighbor: Dependent nearest neighbor,†Appl. Soft Comput., vol. 55, pp. 480–490, Jun. 2017, doi: 10.1016/j.asoc.2017.02.020.

É. O. Rodrigues, “Combining Minkowski and Chebyshev: New distance proposal and survey of distance metrics using k-nearest neighbours classifier,†Pattern Recognit. Lett., vol. 110, pp. 66–71, Jul. 2018, doi: 10.1016/j.patrec.2018.03.021.

O. A. Akanbi, I. S. Amiri, and E. Fazeldehkordi, “Research Methodology,†in A Machine-Learning Approach to Phishing Detection and Defense, Elsevier, 2015, pp. 35–43. doi: 10.1016/B978-0-12-802927-5.00003-4.

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Published

2023-01-28

Issue

Vol. 7 No. 1 (2023): Januari 2023

Section

Articles

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