Mobile-based Primate Image Recognition using CNN

Wiranda, Nuruddin; Putra, Agfianto Eko

doi:10.22146/ijccs.65640

Open access Jurnal SINTA 2

Mobile-based Primate Image Recognition using CNN

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 16 No. 2 (2022) · pp. 149–158

DOI: 10.22146/ijccs.65640
ISSN: 2460-7258, 1978-1520
Published: 2022-04-30
Publisher: IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language: eng
License: creativecommons.org/licenses/by-sa/4.0

Abstract

Six out of 25 species of primates most endangered are in Indonesia. Six of these primates are namely Orangutan, Lutung, Bekantan, Tarsius tumpara, Kukang, and Simakobu. Three of the six primates live mostly on the island of Borneo. One form of preservation of primate treasures found in Kalimantan is by conducting studies on primate identification. In this study, an android app was developed using the CNN method to identify primate species in Kalimantan wetlands. CNN is used to extract spatial features from primate images to be very efficient for image identification problems. The data set used in this study is ImageNets, while the model used is MobileNets. The application was tested using two scenarios, namely using photos and video recordings. Photos were taken directly, then reduced to a resolution of 256 x 256. Then, videos were taken in approximately 10 to 30 seconds with two megapixel camera resolution. The results obtained was an average accuracy of 93.6% when using photos and 79% when using video recordings. After calculating the accuracy, the usability test using SUS was performed. Based on the SUS results, it is known that the application developed is feasible to use.

Keywords

Collaboration network of the authors

9 authors · 10 co-authorship links · 6 across different organisations

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Universitas Gadjah Mada
Universitas Halu Oleo
Lambung Mangkurat University
Indonesia Open University
Telkom University

Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles

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Co-author	Organisation	Joint articles
Rahmat Ramadhan	Universitas Halu Oleo	1
Ika Purwanti Ningrum	Universitas Halu Oleo	1
Dian Nursantika	Indonesia Open University	1
Endang Anggiratih	Universitas Gadjah Mada	1
Wahyono Wahyono	Universitas Gadjah Mada	1
Muhammad Dzulfikar Fauzi	Universitas Gadjah Mada	1
Suyanto Suyanto	Telkom University	1

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">Mobile-based Primate Image Recognition using CNN</dc:title>
	<dc:creator>Wiranda, Nuruddin</dc:creator>
	<dc:creator>Putra, Agfianto Eko</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Image Recognition; Mobile-based; CNN</dc:subject>
	<dc:description xml:lang="en-US">Six out of 25 species of primates most endangered are in Indonesia. Six of these primates are namely Orangutan, Lutung, Bekantan, Tarsius tumpara, Kukang, and Simakobu. Three of the six primates live mostly on the island of Borneo. One form of preservation of primate treasures found in Kalimantan is by conducting studies on primate identification. In this study, an android app was developed using the CNN method to identify primate species in Kalimantan wetlands. CNN is used to extract spatial features from primate images to be very efficient for image identification problems. The data set used in this study is ImageNets, while the model used is MobileNets. The application was tested using two scenarios, namely using photos and video recordings. Photos were taken directly, then reduced to a resolution of 256 x 256. Then, videos were taken in approximately 10 to 30 seconds with two megapixel camera resolution. The results obtained was an average accuracy of 93.6% when using photos and 79% when using video recordings. After calculating the accuracy, the usability test using SUS was performed. Based on the SUS results, it is known that the application developed is feasible to use.</dc:description>
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	<dc:date>2022-04-30</dc:date>
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	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 16, No 2 (2022): April; 149-158</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/65640/33706</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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