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Open access Jurnal SINTA 3

IMPLEMENTASI DEEP LEARNING DENGAN METODE CONVOLUTIONAL NEURAL NETWORK UNTUK IDENTIFIKASI OBJEK SECARA REAL TIME BERBASIS ANDROID

SemanTIK: Teknik Informasi · Vol. 5 No. 2 (2019) · pp. 237–244

DOI
10.55679/semantik.v5i2.7554
ISSN
2502-8928, 2460-1446
Published
2019-12-18
Publisher
Informatics Engineering Department of Halu Oleo University
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The development of artificial intelligence has now undergone significant changes. This underlies the birth of a method to deal with object detection in real time with high accuracy. Basically Deep Learning is the implementation of the basic concept of Machine Learning which applies the smart algorithm with more layers between the input layer and the output layer. Convolutional Neural Network (CNN) is one method of Deep learning (DL) that can be used to detect and recognize an object in a digital image. The ability of CNN is claimed to be the best model to solve the problem of object detection and object recognition because it is a development of the backpropagation method and does not require large computation in the process. The results obtained in this study that android applications can run well with an accuracy of 92.33% can be seen from the test results using the 10-fold cross validation method, all available menus can be run and the mention of object labels is appropriate for image recognition and classification. Calculation of precision and recall has good values, each at 97.51% and 94.33%. In the classification process, objects that do not exist in datasets that have been modeled by the system will be null or unrecognized, especially in the image of an object captured by an Android camera that has many objects and is close together

Keywords

Collaboration network of the authors

40 authors · 133 co-authorship links · showing the 40 most connected

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

  • Universitas Halu Oleo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Jumadil NangiUniversitas Halu Oleo7
Bambang PramonoUniversitas Halu Oleo6
Natalis RansiUniversitas Halu Oleo5
Ika Purwanti NingrumUniversitas Halu Oleo5
La SurimiUniversitas Halu Oleo4
LM. Fid AksaraUniversitas Halu Oleo4
LM Bahtiar AksaraUniversitas Halu Oleo3
Isnawaty IsnawatyUniversitas Halu Oleo3
Rahmat RamadhanUniversitas Halu Oleo2
L.M. Fid AksaraUniversitas Halu Oleo2
Rifa’atus ShalihahUniversitas Halu Oleo2
Nurul Hajra Hadijah HajraUniversitas Halu Oleo1
SutardiUniversitas Halu Oleo1
Ageng Arya Khrysna DwipanggaUniversitas Halu Oleo1
IsnawatyUniversitas Halu Oleo1
Alif H. La JumaniUniversitas Halu Oleo1
Feri FadlinAffiliation unknown1
Suparjo SuparjoAffiliation unknown1
Arman ArmanUniversitas Halu Oleo1
Edi CahyonoUniversitas Halu Oleo1
La Ode Muh. Golok JayaUniversitas Halu Oleo1
Rahma Surya AlamAffiliation unknown1
Muh.Muchsin Al-As'Ad MirsyahAffiliation unknown1
Wa Ode Wahyuni MakmunUniversitas Halu Oleo1
Sutardi SutardiUniversitas Halu Oleo1
Chindy Elfiana Suci NatasiyaAffiliation unknown1
Ilham Julian EfendiUniversitas Halu Oleo1
Laode Muhammad Golok JayaAffiliation unknown1
Uniadi MangidiAffiliation unknown1
Agustan AgustanUniversitas Halu Oleo1
Abdul KarimAffiliation unknown1
Mustarum MusaruddinUniversitas Halu Oleo1
Muhammad Ishak JaelaniUniversitas Halu Oleo1
Alfian AlfianAffiliation unknown1
Chaeril AksanUniversitas Halu Oleo1
Iin Juliana BudimanUniversitas Halu Oleo1
Mamta Culkari PudingAffiliation unknown1

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://ojs.uho.ac.id/index.php/semantik/oai, first harvested 29 Okt 2026, last seen 29 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">IMPLEMENTASI DEEP LEARNING  DENGAN METODE CONVOLUTIONAL NEURAL NETWORK UNTUK IDENTIFIKASI OBJEK SECARA REAL TIME BERBASIS ANDROID</dc:title>
	<dc:creator>Alam, Indra Fransiskus</dc:creator>
	<dc:creator>Sarita, Muhammad Ihsan</dc:creator>
	<dc:creator>Sajiah, Adha Mashur</dc:creator>
	<dc:subject xml:lang="en-US">Software Engineering; Intelligent Systems; Information System;</dc:subject>
	<dc:description xml:lang="en-US">The development of artificial intelligence has now undergone significant changes. This underlies the birth of a method to deal with object detection in real time with high accuracy. Basically Deep Learning is the implementation of the basic concept of Machine Learning which applies the smart algorithm with more layers between the input layer and the output layer. Convolutional Neural Network (CNN) is one method of Deep learning (DL) that can be used to detect and recognize an object in a digital image. The ability of CNN is claimed to be the best model to solve the problem of object detection and object recognition because it is a development of the backpropagation method and does not require large computation in the process. The results obtained in this study that android applications can run well with an accuracy of 92.33% can be seen from the test results using the 10-fold cross validation method, all available menus can be run and the mention of object labels is appropriate for image recognition and classification. Calculation of precision and recall has good values, each at 97.51% and 94.33%. In the classification process, objects that do not exist in datasets that have been modeled by the system will be null or unrecognized, especially in the image of an object captured by an Android camera that has many objects and is close together</dc:description>
	<dc:publisher xml:lang="en-US">Informatics Engineering Department of Halu Oleo University</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2019-12-18</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs.uho.ac.id/index.php/semantik/article/view/7554</dc:identifier>
	<dc:identifier>10.55679/semantik.v5i2.7554</dc:identifier>
	<dc:source xml:lang="en-US">SemanTIK : Teknik Informasi; Vol 5, No 2 (2019): semanTIK; 237-244</dc:source>
	<dc:source>2502-8928</dc:source>
	<dc:source>2460-1446</dc:source>
	<dc:source>10.55679/semantik.v5i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs.uho.ac.id/index.php/semantik/article/view/7554/pdf</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 semanTIK</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>