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Genetic algorithm-based chicken manure weight prediction system development

IAES International Journal of Artificial Intelligence · Vol. 15 No. 2 (2026) · hlm. 1247–1260

DOI
10.11591/ijai.v15.i2.pp1247-1260
ISSN
2252-8938, 2089-4872
Terbit
2026-04-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This research presents design and implementation of internet of things (IoT) based monitoring and predictive system for evaluating chicken manure weight and environmental conditions in poultry housing. The proposed system integrates MQ-137 sensor for ammonia detection, DHT22 sensor for temperature and humidity measurement, and load cell modules for manure weight monitoring. All sensor data are transmitted in real time to cloud platform, enabling continuous environmental assessment. A 30-day experimental study was conducted using two controlled chicken drum models, each containing 15 broiler chickens and provided with different feed types to observe variations in manure production and air quality. Sensor calibration results indicate high accuracy, with average error of 0.31% for ammonia readings and 0.10% for manure weight measurement. Experimental findings show that feed type A generates lower manure weight, reduced ammonia concentration, and more stable temperature conditions compared to feed type B, suggesting improved feed efficiency and better overall chicken health. A genetic algorithm (GA) was employed to optimize regression model predicting manure weight using ammonia concentration and temperature as input features. The GA-optimized model achieved strong predictive performance, with root mean square error (RMSE) of 0.358 g and coefficient of determination (R2) value of 0.992. The results demonstrate that proposed system provides reliable, scalable, and data-driven solution for smart poultry monitoring and early health detection.

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  • Universitas Halu Oleo
  • Bandung Institute of Technology
  • Politeknik Negeri Malang
  • University of Brawijaya
  • Universitas Jambi
  • Hasanuddin University
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Rekan penulisOrganisasiArtikel bersama
Dian Arisandy Eka Putra SembiringAfiliasi tidak diketahui8
Adrian TawaiUniversitas Halu Oleo7
Robi HendraAfiliasi tidak diketahui7
Toto SubrotoAfiliasi tidak diketahui5
Hansein Arif WijayaAfiliasi tidak diketahui4
La Ode Muhammad ElwanUniversitas Halu Oleo3
La Ode AslimUniversitas Halu Oleo3
Muhammad FauzanUniversitas Jambi3
Umi BarorohAfiliasi tidak diketahui3
Shinta KusumawardaniAfiliasi tidak diketahui3
Sartono SartonoUniversitas Halu Oleo2
Nur Fitriani Usdyana AtthamidAfiliasi tidak diketahui2
Sri IndriatiAfiliasi tidak diketahui2
Masvil TomiAfiliasi tidak diketahui2
Lely MardiyantiAfiliasi tidak diketahui2
Ali SadikinAfiliasi tidak diketahui2
Hamka HamkaAfiliasi tidak diketahui2
Akhlaque AhmadBandung Institute of Technology1
B. RiyantoBandung Institute of Technology1
Achmad Saefuddin NoerBandung Institute of Technology1
Herman TolleUniversity of Brawijaya1
Mokhamad Hadi WijayaUniversity of Brawijaya1
Nailul MunaPoliteknik Negeri Malang1
Barra RafiAfiliasi tidak diketahui1
Arwin SumariAfiliasi tidak diketahui1
Agil EvanAfiliasi tidak diketahui1
Isnawaty IsnawatyAfiliasi tidak diketahui1
Rahmat RamadhanUniversitas Halu Oleo1
la ode Maksar MuhurunaAfiliasi tidak diketahui1
Ambo AkoHasanuddin University1
Fitri KhairunnisaAfiliasi tidak diketahui1
HardiwinotoAfiliasi tidak diketahui1
Siti Zaharah RosliAfiliasi tidak diketahui1
Diode YonataAfiliasi tidak diketahui1
Boby PranataAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Genetic algorithm-based chicken manure weight prediction  system development</dc:title>
	<dc:creator>Hudaya, Rida</dc:creator>
	<dc:creator>Wirayoga, Septriandi</dc:creator>
	<dc:creator>Sarosa, Moechammad</dc:creator>
	<dc:creator>Yusuf, Muhammad</dc:creator>
	<dc:creator>Prayugo, Armanda Dwi</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Environmental sensing; Genetic algorithm; Internet of things; Manure weight prediction; MQ-137 sensor; Poultry monitoring; Smart farming</dc:subject>
	<dc:description xml:lang="en-US">This research presents design and implementation of internet of things (IoT) based monitoring and predictive system for evaluating chicken manure weight and environmental conditions in poultry housing. The proposed system integrates MQ-137 sensor for ammonia detection, DHT22 sensor for temperature and humidity measurement, and load cell modules for manure weight monitoring. All sensor data are transmitted in real time to cloud platform, enabling continuous environmental assessment. A 30-day experimental study was conducted using two controlled chicken drum models, each containing 15 broiler chickens and provided with different feed types to observe variations in manure production and air quality. Sensor calibration results indicate high accuracy, with average error of 0.31% for ammonia readings and 0.10% for manure weight measurement. Experimental findings show that feed type A generates lower manure weight, reduced ammonia concentration, and more stable temperature conditions compared to feed type B, suggesting improved feed efficiency and better overall chicken health. A genetic algorithm (GA) was employed to optimize regression model predicting manure weight using ammonia concentration and temperature as input features. The GA-optimized model achieved strong predictive performance, with root mean square error (RMSE) of 0.358 g and coefficient of determination (R2) value of 0.992. The results demonstrate that proposed system provides reliable, scalable, and data-driven solution for smart poultry monitoring and early health detection.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2026-04-01</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://ijai.iaescore.com/index.php/IJAI/article/view/27465</dc:identifier>
	<dc:identifier>10.11591/ijai.v15.i2.pp1247-1260</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 15, No 2: April 2026; 1247-1260</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v15.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/27465/15094</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Rida Hudaya, Septriandi Wirayoga, Moechammad Sarosa, Muhammad Yusuf,   Armanda Dwi Prayugo</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>