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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) · hlm. 436–442

DOI
10.5398/tasj.2022.45.4.436
ISSN
2615-790X, 2615-787X
Terbit
2022-11-28
Penerbit
Faculty of Animal Science, IPB University
Bahasa
eng

Abstrak

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

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  • IPB University
  • Universitas Halu Oleo
  • National Research and Innovation Agency
  • University of Brawijaya
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Rekan penulisOrganisasiArtikel bersama
Panca Dewi Manu Hara KartiIPB University8
Lailan SyaufinaIPB University5
Iwan PrihantoroIPB University4
Dewi Apri AstutiIPB University4
Annisa AnnisaIPB University3
A. M. FuahIPB University3
Iwan HerdiawanNational Research and Innovation Agency3
Shelvie Nidya NeymanIPB University3
Yani NurhadryaniIPB University2
Didid DiapariIPB University2
Sri RahayuUniversity of Brawijaya2
Karti PDMHAfiliasi tidak diketahui2
. SuryaniUniversitas Halu Oleo2
T ToharmatIPB University2
D EvvyernieIPB University2
Rachmat SomanjayaIPB University2
Idat Galih PermanaIPB University2
Andri ErnawatiIPB University2
Agus BuonoIPB University1
Mustakim MustakimUniversitas Halu Oleo1
Kudang Boro SeminarIPB University1
Melva Linda AritonangIPB University1
Nugraha Edhi SuyatmaIPB University1
I Nyoman Rai Widartha KesumaIPB University1
Addini YusmarIPB University1
Zuliar EfendiIPB University1
Alusyanti PrimawatiIPB University1
Yoga HerawanIPB University1
Ahmad Bintang ArifIPB University1
Hari Agung AdriantoIPB University1
Assad HidayatIPB University1
Sony Hartono WijayaIPB University1
Dody HaryadiAfiliasi tidak diketahui1
Budi SetiawanAfiliasi tidak diketahui1
Hajar HajarAfiliasi tidak diketahui1
Rachmat SomanjayaAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.ipb.ac.id/index.php/tasj/oai, pertama di-harvest 10 Nov 2026, terakhir terlihat 10 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">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>