Sistem Identifikasi Kesegaran dan Jenis Ikan dengan Metode K-Nearest Neighbor Berdasarkan Citra Mata dan Bentuk Ikan
DOI:
https://doi.org/10.56795/fortech.v4i1.383Keywords:
Fish Freshness, Image Processing, Classification, ConveyorAbstract
Fish is a food commodity that needs attention, to improve the quality of food production, especially the fish itself. The level of freshness of fish greatly affects the quality of food production both at the household and industrial levels, as well as determining the feasibility of the fish for processing and consumption. Currently, to determine the level of quality of fish freshness, it is still done conventionally by humans, while those who have used tools but still have deficiencies in both the level of accuracy and also the features they have are still small. In this study, a tool or system design was carried out that could identify the freshness level of a fish based on eye images taken using a webcam camera or the like as input from data to be processed using image processing. In addition, the system is given additional features to be able to identify the type of fish. So that this additional feature can help facilitate identification all at once. To classify the method used is the K-Nearest Neighbor method. The results of the data processing will be displayed in the form of a sorting system for output. In the research results obtained from the system this time from 280 datasets for identification of freshness were tested on 50 images with a success rate of 96% for fresh and 84% for rotten. While the results of identification of fish species from 50 images of test data from three types of fish obtained a success rate of 97.7% with a value of k = 5.
References
E. Suprayitno, “the Influence of Fish Mortality on the Freshness of Fish,” Int. J. Res. -GRANTHAALAYAH, vol. 6, no. 2, pp. 80–85, 2018, doi: 10.29121/granthaalayah.v6.i2.2018.1547.
I. Indrabayu, M. Niswar, and A. A. Aman, “Sistem Pendeteksi Kesegaran Ikan Bandeng Menggunakan Citra,” J. INFOTEL - Inform. Telekomun. Elektron., vol. 8, no. 2, pp. 170–179, 2016, doi: 10.20895/infotel.v8i2.119.
I. C. Navotas, C. N. V. Santos, E. J. M. Balderrama, F. E. B. Candido, A. J. E. Villacanas, and J. S. Velasco, “Fish identification and freshness classification through image processing using artificial neural network,” ARPN J. Eng. Appl. Sci., vol. 13, no. 18, pp. 4912–4922, 2018.
M. R. Kumaseh, L. Latumakulita, and N. Nainggolan, “Segmentasi Citra Digital Ikan Menggunakan Metode Thresholding,” J. Ilm. Sains, vol. 13, no. 1, p. 74, 2013, doi: 10.35799/jis.13.1.2013.2057.
A. Kalista, A. Redjo, and U. Rosidah, “Aplication of Image Processing to Determine The Freshness of Tilapia Fish (Oreochromis niloticus),” J. Pengolah. Has. Perikan. Indones., vol. 22, no. 2, pp. 229–235, 2019, doi: 10.17844/jphpi.v22i2.27364.
M. Sarosa and N. Muna, “Implementasi Algoritma You Only Look Once ( Yolo ) Untuk Implementation of You Only Look Once ( Yolo ) Algorithm for,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 4, pp. 787–792, 2021, doi: 10.25126/jtiik.202184407.
M. L. Nazilly, B. Rahmat, and E. Y. Puspaningrum, “Implementasi Algoritma Yolo (You Only Look Once) Untuk Deteksi Api,” J. Inform. dan Sist. Inf., vol. 1, no. 1, pp. 81–91, 2020.
Informatikalogi, “Algoritma K-Nearest Neighbor (K-NN) | INFORMATIKALOGI.” p. 1, 2017.
T. D. Novianto and I. M. S. Erawan, “Perbandingan Metode Klasifikasi pada Pengolahan Citra Mata Ikan Tuna,” pp. 216–223, 2020.
R. D. Nurfita and G. Ariyanto, “Implementasi Deep Learning berbasis Tensorflow untuk Pengenalan Sidik Jari,” Emit. J. Tek. Elektro, vol. 18, no. 1, pp. 22–27, 2018, doi: 10.23917/emitor.v18i01.6236.
