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Study of the Effect of Physical Parameters on Commercial Hydroponics Based on Internet of Things (IoT): A Case Study of Bok Coy Plants (Brassica rapa) and Water Spinach (Ipomoea Aquatica)

Journal of Mathematical and Fundamental Sciences · Vol. 54 No. 2 (2023) · pp. 275–289

DOI
10.5614/j.math.fund.sci.2022.54.2.5
ISSN
2338-5510, 2337-5760
Published
2023-04-17
Publisher
Directorate for Research and Innovation (DRI) ITB
Language
eng

Abstract

Population growth causes the demand for food to increase. One solution that can be applied is agriculture with hydroponic technology. To increase production efficiency, one must know the physical parameters that most influence the production process. This research used an IoT system to gather accurate and precise measurement data of physical parameters to be used as a dataset for machine learning. The dataset consisted of light intensity, humidity, air temperature, and total dissolved solids (TDS). Plant growth was measured by leaf area of the plant, number of leaves, and plant stem length every 3 to 4 days. The models used in the machine learning process were linear regression, polynomial regression, and random forest regression. The machine learning results showed that the best model for predicting plant growth was random forest regression with an MAE of 8.3% and an R2 of 0.93, for both bok coy and water spinach. The variables that influence growth the most are TDS and light intensity. According to the relationship between TDS gradient and plant growth gradient, the most optimal growth can be achieved by raising the TDS gradient or by maintaining a high TDS, which can be achieved by adding nutrient solution to the tank regularly.

Keywords

Collaboration network of the authors

33 authors · 182 co-authorship links · 39 across different organisations

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

  • National Research and Innovation Agency
  • Bandung Institute of Technology
  • IPB University
  • National University of Malaysia
  • Indonesian Institute of Sciences
  • Telkom University
  • Universitas Widyatama
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Mitra DjamalBandung Institute of Technology5
Abdul MuidBandung Institute of Technology4
Agus RisdiyantoNational Research and Innovation Agency3
Noviadi Arief RachmanNational Research and Innovation Agency3
Ahmad FudholiNational University of Malaysia3
Agus JunaediNational Research and Innovation Agency3
Rudi DarussalamNational Research and Innovation Agency2
Muhamamad Reza Ramadhani RaharjoBandung Institute of Technology2
Anwar MuqorobinIndonesian Institute of Sciences1
Bambang SusantoNational Research and Innovation Agency1
Harjono Priyo SantosaNational Research and Innovation Agency1
Erwin SusantoTelkom University1
Dalmasius Ganjar SubagioNational Research and Innovation Agency1
Muhammad KasimNational Research and Innovation Agency1
Udin KomarudinUniversitas Widyatama1
Wahyu SrigutomoBandung Institute of Technology1
Irwan MeilanoAffiliation unknown1
Ari SetiawanAffiliation unknown1
Irzaman IrzamanIPB University1
Perdinan PerdinanIPB University1
Retna ApsariAffiliation unknown1
Wiwit SuryantoAffiliation unknown1
Imam SuyantoAffiliation unknown1
Heriansyah PutraAffiliation unknown1
Herlan DarmawanAffiliation unknown1
Maria EvitaAffiliation unknown1
Moh YasinAffiliation unknown1
Wahyudi WahyudiAffiliation 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://journals.itb.ac.id/index.php/jmfs/oai, first harvested 9 Nov 2026, last seen 9 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Study of the Effect of Physical Parameters on Commercial Hydroponics Based on Internet of Things (IoT): A Case Study of Bok Coy Plants (Brassica rapa) and Water Spinach (Ipomoea Aquatica)</dc:title>
	<dc:creator>Budiman, Maman</dc:creator>
	<dc:creator>Partogi, Efraim</dc:creator>
	<dc:creator>Kristi, Ant. Ardath </dc:creator>
	<dc:creator>Anggara, Prianka</dc:creator>
	<dc:creator>Aminah, Nina Siti</dc:creator>
	<dc:subject xml:lang="en-US">bok coy</dc:subject>
	<dc:subject xml:lang="en-US">hydroponics</dc:subject>
	<dc:subject xml:lang="en-US">IoT</dc:subject>
	<dc:subject xml:lang="en-US">machine learning</dc:subject>
	<dc:subject xml:lang="en-US">water spinach</dc:subject>
	<dc:subject xml:lang="en-US">Physics</dc:subject>
	<dc:description xml:lang="en-US">Population growth causes the demand for food to increase. One solution that can be applied is agriculture with hydroponic technology. To increase production efficiency, one must know the physical parameters that most influence the production process. This research used an IoT system to gather accurate and precise measurement data of physical parameters to be used as a dataset for machine learning. The dataset consisted of light intensity, humidity, air temperature, and total dissolved solids (TDS). Plant growth was measured by leaf area of the plant, number of leaves, and plant stem length every 3 to 4 days. The models used in the machine learning process were linear regression, polynomial regression, and random forest regression. The machine learning results showed that the best model for predicting plant growth was random forest regression with an MAE of 8.3% and an R2 of 0.93, for both bok coy and water spinach. The variables that influence growth the most are TDS and light intensity. According to the relationship between TDS gradient and plant growth gradient, the most optimal growth can be achieved by raising the TDS gradient or by maintaining a high TDS, which can be achieved by adding nutrient solution to the tank regularly.</dc:description>
	<dc:publisher xml:lang="en-US">Directorate for Research and Innovation (DRI) ITB</dc:publisher>
	<dc:date>2023-04-17</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jmfs/article/view/18068</dc:identifier>
	<dc:identifier>10.5614/j.math.fund.sci.2022.54.2.5</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Mathematical and Fundamental Sciences; Vol. 54 No. 2 (2022); 275-289</dc:source>
	<dc:source>2338-5510</dc:source>
	<dc:source>2337-5760</dc:source>
	<dc:source>10.5614/j.math.fund.sci.2022.54.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jmfs/article/view/18068/6157</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Journal of Mathematical and Fundamental Sciences</dc:rights>
</oai_dc:dc>