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ANALYSIS OF MACHINE LEARNING ALGORITHM PERFORMANCE IN PREDICTING ULTISOL SOIL NUTRIENTS BASED ON IMPEDANCE VALUES

JOURNAL ONLINE OF PHYSICS · Vol. 9 No. 2 (2024) · pp. 94–101

DOI
10.22437/jop.v9i2.32564
ISSN
2502-2016
Published
2024-05-01
Publisher
Prodi Fisika FST UNJA
Language
en

Abstract

A study comparing the performance of machine learning algorithms to predict soil nutrient values based on soil impedance has been conducted. The algorithm models used include Linear Model, K-Nearest Neighbors (K-NN) with n-neighbors 3, 18, 21, 24, 27, and 30, Decision Tree with max depth 3, and Random Forest with n-estimators 6 and 21. During the training phase, 10 model variations with the best performance were found, including Linear Model, K-NN (n-neighbors), Decision Tree (max depth 3), and Random Forest (n-estimators 6 and 21). In the testing phase, Random Forest (n-estimator 21) showed the best performance with MAE = 0.15%, MSE = 0.09%, RMSE = 0.31%, and accuracy = 99.85%. Regression analysis indicated an R-squared value of 0.924, indicating that most of the variations in soil impedance values can be explained by variations in soil nutrient values. A regression value approaching 1 indicates that the regression model used has a very good ability to explain the variations observed in the data. This indicates that most of the variations in the dependent variable (the variable being predicted, which is the nutrient values) can be explained by the independent variable (the predictor variable, which is the soil impedance values) in the model. Correlation analysis resulted in a strong negative correlation between impedance and Al, Fe, K, Ca, Zn, Ni, Ta, V, Cr, and Mn (values -0.81 to -0.99), while a positive correlation occurred with Mg, Si, S, Cl, Ti, Zr, and Ga (values 0.65 to 0.99). This indicates that an increase in impedance values is generally followed by an increase in nutrient values.

Keywords

Collaboration network of the authors

40 authors · 159 co-authorship links · 3 across different organisations · showing the 40 most connected

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  • Universitas Muslim Indonesia
  • Universitas Halu Oleo
  • Sekolah Tinggi Agama Buddha Negeri Raden Wijaya Wonogiri
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Yoza FendrianiAffiliation unknown9
Linda HandayaniAffiliation unknown8
Rustan RustanAffiliation unknown7
Iful AmriAffiliation unknown6
M. Ficky AfriantoAffiliation unknown6
Mardian PeslinofAffiliation unknown6
Frastica DeswardaniAffiliation unknown5
Tika RestianingsihAffiliation unknown5
Alrizal AlrizalAffiliation unknown5
Maison MaisonAffiliation unknown4
Ilham Jaya WindhanuAffiliation unknown4
Sutrisno SutrisnoSekolah Tinggi Agama Buddha Negeri Raden Wijaya Wonogiri3
Madyawati LatiefAffiliation unknown3
Ichy Lucya RestaAffiliation unknown3
Fernando Mersa PutraAffiliation unknown2
Arnild Augina MekarisceAffiliation unknown2
Oka Lesmana SAffiliation unknown2
Faizar FaridAffiliation unknown2
Sampe NapitupuluAffiliation unknown2
Sarinah PakpahanAffiliation unknown2
Cindy Nur SyaqilaAffiliation unknown2
Ardiyaningsih Puji LestariAffiliation unknown2
Syamsuddin PasamaiUniversitas Muslim Indonesia1
Sri Lestari PoernomoUniversitas Muslim Indonesia1
Mursidin MursidinAffiliation unknown1
Rifai NurAffiliation unknown1
Tanzil TanzilUniversitas Halu Oleo1
Jamaluddin HosUniversitas Halu Oleo1
Fairuz Athaya zahran Tane TaneAffiliation unknown1
Nurhidayah NurhidayahAffiliation unknown1
M. Ficky ApriantoAffiliation unknown1
Nurhidayah NurhidayahAffiliation unknown1
Nurhidayah NurhidayahAffiliation unknown1
Rista Mutia AnggrainiAffiliation unknown1
Nurhidayah NurhidayahAffiliation unknown1
Sherli YurinandaAffiliation unknown1
Zuli RodhiyahAffiliation 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://online-journal.unja.ac.id/index.php/jop/oai, first harvested 6 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">ANALYSIS OF MACHINE LEARNING ALGORITHM PERFORMANCE IN PREDICTING ULTISOL SOIL NUTRIENTS BASED ON IMPEDANCE VALUES</dc:title>
	<dc:creator xml:lang="en">Amanda, Dwi Rahmah</dc:creator>
	<dc:creator xml:lang="en">Samsidar, Samsidar</dc:creator>
	<dc:creator xml:lang="en">Pebralia, Jesi</dc:creator>
	<dc:subject xml:lang="en">Soil Nutrients</dc:subject>
	<dc:subject xml:lang="en">Soil Impedance</dc:subject>
	<dc:subject xml:lang="en">Machine Learning</dc:subject>
	<dc:subject xml:lang="en">Linear Model</dc:subject>
	<dc:subject xml:lang="en">K-Nearest Neighbors</dc:subject>
	<dc:subject xml:lang="en">Decision Tree</dc:subject>
	<dc:subject xml:lang="en">Random Forest</dc:subject>
	<dc:description xml:lang="en">A study comparing the performance of machine learning algorithms to predict soil nutrient values based on soil impedance has been conducted. The algorithm models used include Linear Model, K-Nearest Neighbors (K-NN) with n-neighbors 3, 18, 21, 24, 27, and 30, Decision Tree with max depth 3, and Random Forest with n-estimators 6 and 21. During the training phase, 10 model variations with the best performance were found, including Linear Model, K-NN (n-neighbors), Decision Tree (max depth 3), and Random Forest (n-estimators 6 and 21). In the testing phase, Random Forest (n-estimator 21) showed the best performance with MAE = 0.15%, MSE = 0.09%, RMSE = 0.31%, and accuracy = 99.85%. Regression analysis indicated an R-squared value of 0.924, indicating that most of the variations in soil impedance values can be explained by variations in soil nutrient values. A regression value approaching 1 indicates that the regression model used has a very good ability to explain the variations observed in the data. This indicates that most of the variations in the dependent variable (the variable being predicted, which is the nutrient values) can be explained by the independent variable (the predictor variable, which is the soil impedance values) in the model. Correlation analysis resulted in a strong negative correlation between impedance and Al, Fe, K, Ca, Zn, Ni, Ta, V, Cr, and Mn (values -0.81 to -0.99), while a positive correlation occurred with Mg, Si, S, Cl, Ti, Zr, and Ga (values 0.65 to 0.99). This indicates that an increase in impedance values is generally followed by an increase in nutrient values.</dc:description>
	<dc:publisher xml:lang="en">Prodi Fisika FST UNJA</dc:publisher>
	<dc:date>2024-05-01</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://online-journal.unja.ac.id/jop/article/view/32564</dc:identifier>
	<dc:identifier>10.22437/jop.v9i2.32564</dc:identifier>
	<dc:source xml:lang="en">JOURNAL ONLINE OF PHYSICS; Vol. 9 No. 2 (2024): JOP (Journal Online of Physics) Vol 9 No 2; 94-101</dc:source>
	<dc:source>2502-2016</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://online-journal.unja.ac.id/jop/article/view/32564/17942</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 JOURNAL ONLINE OF PHYSICS</dc:rights>
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