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Estimation of Harvest Time of Forage Sorghum (Sorghum Bicolor) CV. Samurai-2 Using Decision Tree Algorithm

Tropical Animal Science Journal · Vol. 45 No. 4 (2022) · pp. 436–442

DOI
10.5398/tasj.2022.45.4.436
ISSN
2615-790X, 2615-787X
Published
2022-11-28
Publisher
Faculty of Animal Science, IPB University
Language
eng

Abstract

Efforts to improve feed quality by adding additional nutritional supplements can increase production costs due to the increased concentrate prices. Therefore, one option is to combine the main feed with forages containing a high protein source at a low cost, such as Gramineae (e.g., sorghum). This study aims to estimate the harvest time of sorghum when the biomass content, nutrients, and digestibility for livestock are in good condition using a machine learning algorithm, namely a decision tree. The stages of this study include the collection of observation data in the field, preprocessing, modeling, evaluation, and validation. Images and field observations are the primary datasets used. These datasets become the model input for the decision tree algorithm. The results of this study are the classification model for estimating harvest time with an accuracy of 98.86% and the rule that is generated by the decision tree model, the right time to be harvested are in the condition (Day After Planting > 77.5 days AND Day After Planting ≤ 84 days AND Diameter > 26 mm) or (Day After Planting > 84 days AND Height ≤ 138.5 cm AND Leaves > 8.5 pieces) or (Day After Planting > 84 days AND Height > 138.5 cm). In conclusion, the rule generated from the decision tree algorithm can help estimate the fast harvest time of sorghum bicolor cv. Samurai 2.decision tree

Keywords

Collaboration network of the authors

40 authors · 129 co-authorship links · 5 across different organisations · showing the 40 most connected

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

  • Universiti Teknikal Malaysia Melaka
  • Universitas Halu Oleo
  • University of Technology Malaysia
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
P.D.M.H KartiAffiliation unknown7
Iwan PrihantoroAffiliation unknown2
. SuryaniUniversitas Halu Oleo2
T. ToharmatAffiliation unknown2
D EvvyernieAffiliation unknown2
I. G. PermanaAffiliation unknown2
A. ErnawatiAffiliation unknown2
Mohd Fua’ad RahmatUniversity of Technology Malaysia1
Syed Najib Syed SalimUniversiti Teknikal Malaysia Melaka1
Shamsul Anuar ShamsudinUniversiti Teknikal Malaysia Melaka1
Maslan ZainonUniversiti Teknikal Malaysia Melaka1
A. F. M. AminAffiliation unknown1
Achmad FanindiAffiliation unknown1
B.R PrawiradiputraAffiliation unknown1
N.S YunitasariAffiliation unknown1
A.P SariAffiliation unknown1
D KhairunisaAffiliation unknown1
A HaziqAffiliation unknown1
N RahayuAffiliation unknown1
Wasmen ManaluAffiliation unknown1
R. PriyantoAffiliation unknown1
Edit Lesa AditiaAffiliation unknown1
Panca Dewi Maha Rani KartiAffiliation unknown1
N HidayatiAffiliation unknown1
Iwan HerdiawanAffiliation unknown1
D SopandieAffiliation unknown1
I. AnasAffiliation unknown1
P. S. HutapeaAffiliation unknown1
Despal DespalAffiliation unknown1
L. A. SariAffiliation unknown1
R. ChandraAffiliation unknown1
R. ZaheraAffiliation unknown1
Supriyanto .Affiliation unknown1
D.A. AstutiAffiliation unknown1
R. SriagtulaAffiliation unknown1
ZurmiatiAffiliation 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://journal.ipb.ac.id/index.php/tasj/oai, first harvested 10 Nov 2026, last seen 10 Nov 2026.
Original metadata (oai_dc)
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	<dc:title xml:lang="en">Estimation of Harvest Time of Forage Sorghum (Sorghum Bicolor) CV. Samurai-2 Using Decision Tree Algorithm</dc:title>
	<dc:creator xml:lang="en">Suradiradja, K.</dc:creator>
	<dc:creator xml:lang="en">Sitanggang, I. S.</dc:creator>
	<dc:creator xml:lang="en">Abdullah, L.</dc:creator>
	<dc:creator xml:lang="en">Hermadi, I.</dc:creator>
	<dc:subject xml:lang="en">decision tree</dc:subject>
	<dc:subject xml:lang="en">estimated harvest</dc:subject>
	<dc:subject xml:lang="en">forage sorghum</dc:subject>
	<dc:subject xml:lang="en">machine learning</dc:subject>
	<dc:subject xml:lang="en">sorghum bicolor</dc:subject>
	<dc:description xml:lang="en">Efforts to improve feed quality by adding additional nutritional supplements can increase production costs due to the increased concentrate prices. Therefore, one option is to combine the main feed with forages containing a high protein source at a low cost, such as Gramineae (e.g., sorghum). This study aims to estimate the harvest time of sorghum when the biomass content, nutrients, and digestibility for livestock are in good condition using a machine learning algorithm, namely a decision tree. The stages of this study include the collection of observation data in the field, preprocessing, modeling, evaluation, and validation. Images and field observations are the primary datasets used. These datasets become the model input for the decision tree algorithm. The results of this study are the classification model for estimating harvest time with an accuracy of 98.86% and the rule that is generated by the decision tree model, the right time to be harvested are in the condition (Day After Planting &amp;gt; 77.5 days AND Day After Planting ≤ 84 days AND Diameter &amp;gt; 26 mm) or (Day After Planting &amp;gt; 84 days AND Height ≤ 138.5 cm AND Leaves &amp;gt; 8.5 pieces) or (Day After Planting &amp;gt; 84 days AND Height &amp;gt; 138.5 cm). In conclusion, the rule generated from the decision tree algorithm can help estimate the fast harvest time of sorghum bicolor cv. Samurai 2.decision tree</dc:description>
	<dc:publisher xml:lang="en">Faculty of Animal Science, IPB University</dc:publisher>
	<dc:date>2022-11-28</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://journal.ipb.ac.id/tasj/article/view/37652</dc:identifier>
	<dc:identifier>10.5398/tasj.2022.45.4.436</dc:identifier>
	<dc:source xml:lang="en">Tropical Animal Science Journal; Vol. 45 No. 4 (2022): Tropical Animal Science Journal; 436-442</dc:source>
	<dc:source>2615-790X</dc:source>
	<dc:source>2615-787X</dc:source>
	<dc:source>10.5398/tasj.2022.45.4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ipb.ac.id/tasj/article/view/37652/24457</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2022 Tropical Animal Science Journal</dc:rights>
</oai_dc:dc>