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Sistem Penyiraman Otomatis Tanaman Semusim Berbasis Jaringan Saraf Tiruan Multilayer Perceptron

Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) · Vol. 7 No. 1 (2022) · pp. 35–45

DOI
10.33772/jfe.v7i1.24050
ISSN
2502-5562
Published
2022-02-27
Publisher
Universitas Halu Oleo
Language
eng
License
creativecommons.org/licenses/by/4.0

Abstract

In general, farmers water plants when the conditions are met, such as dry soil, no rain, and cold temperatures. One of the efficient ways to control it is to use an artificial neural network-based automatic plant watering system. The purpose of this study was to determine the success of artificial neural networks as decision-makers to water plants automatically. The stages of designing an automatic watering system based on an artificial neural network were to build software including artificial neural network modeling and Arduino microcontroller programming, automatically watering tools, evaluating tool performance, and testing tools in real-time. The test results show that the artificial neural network-based automatic plant watering system can water plants according to the given input pattern. The artificial neural network structure obtained is three neurons in the input layer, eight neurons in the hidden layer, and one neuron in the output layer. The artificial neural network-based automatic plant watering system succeeded in automatically watering two areas of land that the success rate is a 100%.Keyword— Automatic Watering, Microcontroller, ANN, Annual Crops.

Keywords

Collaboration network of the authors

22 authors · 45 co-authorship links

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
Achmad Nur AliansyahUniversitas Halu Oleo2
Nita Zelfia Dinianti Luzi MulyawatiUniversitas Halu Oleo2
Supriadi SabuktionoAffiliation unknown2
Muh. Nadzirin Anshari NurUniversitas Halu Oleo1
Edwin SetiawanAffiliation unknown1
Abdul DjoharUniversitas Halu Oleo1
Agustinus LolokUniversitas Halu Oleo1
Ahmad RoykhanAffiliation unknown1
Masykur KimsanUniversitas Halu Oleo1
Ica Capriyati HusenAffiliation unknown1
La SurimiUniversitas Halu Oleo1
La Ode Muhammad Bahtiar AksaraUniversitas Halu Oleo1
Subardin SubardinAffiliation unknown1
Hasmina Tari MokuiUniversitas Halu Oleo1
Tachrir TachrirUniversitas Halu Oleo1
Ranno Marlany RachmanUniversitas Halu Oleo1
Feliks Eldad LarobuUniversitas Halu Oleo1
AsminarAffiliation unknown1
Indra GaluguAffiliation 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/JFE/oai, first harvested 31 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Sistem Penyiraman Otomatis Tanaman Semusim Berbasis Jaringan Saraf Tiruan Multilayer Perceptron</dc:title>
	<dc:creator>Zakaria, Mamang</dc:creator>
	<dc:creator>Pagiling, Luther</dc:creator>
	<dc:creator>Siti Nur Alam, Wa Ode</dc:creator>
	<dc:subject xml:lang="en-US">Automatic Watering, Microcontroller, ANN, Annual Crops.</dc:subject>
	<dc:description xml:lang="en-US">In general, farmers water plants when the conditions are met, such as dry soil, no rain, and cold temperatures. One of the efficient ways to control it is to use an artificial neural network-based automatic plant watering system. The purpose of this study was to determine the success of artificial neural networks as decision-makers to water plants automatically. The stages of designing an automatic watering system based on an artificial neural network were to build software including artificial neural network modeling and Arduino microcontroller programming, automatically watering tools, evaluating tool performance, and testing tools in real-time. The test results show that the artificial neural network-based automatic plant watering system can water plants according to the given input pattern. The artificial neural network structure obtained is three neurons in the input layer, eight neurons in the hidden layer, and one neuron in the output layer. The artificial neural network-based automatic plant watering system succeeded in automatically watering two areas of land that the success rate is a 100%.Keyword— Automatic Watering, Microcontroller, ANN, Annual Crops.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Halu Oleo</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2022-02-27</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs.uho.ac.id/index.php/JFE/article/view/24050</dc:identifier>
	<dc:identifier>10.33772/jfe.v7i1.24050</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali); Vol 7, No 1 (2022): Jurnal Elektroda Vol 7 No 1; 35-45</dc:source>
	<dc:source>2502-5562</dc:source>
	<dc:source>10.33772/jfe.v7i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs.uho.ac.id/index.php/JFE/article/view/24050/pdf</dc:relation>
	<dc:relation>https://ojs.uho.ac.id/index.php/JFE/article/downloadSuppFile/24050/4375</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Mamang Zakaria, Luther Pagiling, Wa Ode Siti Nur Alam</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>