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Fishku Apps: Fishes Freshness Detection Using CNN With MobilenetV2

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 17 No. 1 (2023) · pp. 67–78

DOI
10.22146/ijccs.80049
ISSN
2460-7258, 1978-1520
Published
2023-02-23
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Marine fish are one of the most promising economic commodities for the Indonesian economy. Marine fish will decrease in protein content along with the decreasing level of freshness of the fish that will be consumed. There are still many people who do not know about the classification of fresh and unfresh fish, so we need a system that can classify which fish are fresh and which are not. Previous studies have succeeded in classifying tuna using a convolutional neural network (CNN) algorithm with an accuracy of 90%. In the preprocessing stage of this research, segmentation is carried out, which aims to separate the object to be studied and the background image, then feature extraction is carried out using a color moment, which aims to get the value of the object to be studied. This research was conducted to increase the accuracy value in the freshness classification of tuna and also to add some fish for freshness detection, such as mackerel and milkfish, using the MobilenetV2. The results were able to produce accuracy of 97%, 94%, and 93% for each fish. The freshness detection method in this study has been implemented in the Fishku mobile-based application.

Keywords

Collaboration network of the authors

22 authors · 43 co-authorship links · 15 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Syarif Hidayatullah State Islamic University Jakarta
  • Universitas Dian Nuswantoro
  • Udayana University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ghulam ImanuddinUniversitas Gadjah Mada1
Suci Karunia PrilistyaUniversitas Gadjah Mada1
Azhari SnUniversitas Gadjah Mada1
Winda YulitaUniversitas Gadjah Mada1
Nur Azizul HaqimiUniversitas Gadjah Mada1
Nur RokhmanUniversitas Gadjah Mada1
Edi WinarkoUniversitas Gadjah Mada1
Sholikin Ady ChandraUniversitas Gadjah Mada1
Mhd. Theo Ari BangsaUniversitas Gadjah Mada1
Yohanes SuyantoUniversitas Gadjah Mada1
Khabib MustofaUniversitas Gadjah Mada1
Amalia Nur AnggraeniUniversitas Gadjah Mada1
Huda MustakimUniversitas Gadjah Mada1
Dika Permana PutraUniversitas Gadjah Mada1
Nur FadilahUniversitas Gadjah Mada1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Fishku Apps: Fishes Freshness Detection Using CNN With MobilenetV2</dc:title>
	<dc:creator>Hanifa, Muthia Farah</dc:creator>
	<dc:creator>Ramadhan, Anugrah Tri</dc:creator>
	<dc:creator>Husna, Ni’Matul</dc:creator>
	<dc:creator>Widiyono, Nabila Apriliana</dc:creator>
	<dc:creator>Mubarak, Rhamdan Syahrul</dc:creator>
	<dc:creator>Putri, Adisti Anjani</dc:creator>
	<dc:creator>Priyanta, Sigit</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Fresh fish; CNN; MobilenetV2</dc:subject>
	<dc:description xml:lang="en-US">Marine fish are one of the most promising economic commodities for the Indonesian economy. Marine fish will decrease in protein content along with the decreasing level of freshness of the fish that will be consumed. There are still many people who do not know about the classification of fresh and unfresh fish, so we need a system that can classify which fish are fresh and which are not. Previous studies have succeeded in classifying tuna using a convolutional neural network (CNN) algorithm with an accuracy of 90%. In the preprocessing stage of this research, segmentation is carried out, which aims to separate the object to be studied and the background image, then feature extraction is carried out using a color moment, which aims to get the value of the object to be studied. This research was conducted to increase the accuracy value in the freshness classification of tuna and also to add some fish for freshness detection, such as mackerel and milkfish, using the MobilenetV2. The results were able to produce accuracy of 97%, 94%, and 93% for each fish. The freshness detection method in this study has been implemented in the Fishku mobile-based application.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-02-23</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/80049</dc:identifier>
	<dc:identifier>10.22146/ijccs.80049</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 17, No 1 (2023): January; 67-78</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/80049/35782</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>