Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jusikom : Jurnal Sistem Komputer Musirawas
Ramdhani, Nida Mardhiyah
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
IDENTIFIKASI CITRA IKAN UNTUK MENDETEKSI KESEGARAN IKAN LAUT MENGGUNAKAN METODE HUE SATURATION VALUE DAN K-NEAREST NEIGHBOR Pertiwi, Marisha; Ramdhani, Nida Mardhiyah; Manik, Agustina Sartika Yos Ekaristi
Jusikom : Jurnal Sistem Komputer Musirawas Vol 9 No 2 (2024): Jusikom : Jurnal Sistem Komputer Musirawas DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v9i2.2389

Abstract

Marine fishery resources contribute to food needs, especially for local communities. The Riau Islands is one of the provinces with abundant marine products but only has 1 fishing port, which can trigger an increase in fish prices in the market and for consumers because the costs incurred by ship owners are getting higher. In addition, it will also cause a decrease in the quality of fish or fish that are not fresh because of the long production chain, which takes a lot of time. Currently, identification of fish freshness is still done manually by fishermen, traders, and consumers. This will certainly make it a little difficult to distinguish between fresh fish and fish that are no longer fresh. One way to recognize and identify fish freshness in the field of informatics is to use image processing. This study will create an image processing system to identify fish freshness using the hue saturation value (HSV) and k-nearest neighbor (K-NN) methods. Based on the results of testing marine fish images, namely mackerel and tuna, which were carried out using the HSV and K-NN methods, it was found that the identification of the quality of freshness of marine fish using the Hue, Saturation, and Value (HSV) color feature extraction was successfully applied with an accuracy value of 100% for the test image of mackerel and 95% for the test image of tuna.
Co-Authors Manik, Agustina Sartika Yos Ekaristi Marisha Pertiwi