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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) · hlm. 94–101

DOI
10.22437/jop.v9i2.32564
ISSN
2502-2016
Terbit
2024-05-01
Penerbit
Prodi Fisika FST UNJA
Bahasa
en

Abstrak

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.

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  • Universitas Muslim Indonesia
  • Universitas Halu Oleo
  • Sekolah Tinggi Agama Buddha Negeri Raden Wijaya Wonogiri
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Yoza FendrianiAfiliasi tidak diketahui9
Linda HandayaniAfiliasi tidak diketahui8
Rustan RustanAfiliasi tidak diketahui7
Iful AmriAfiliasi tidak diketahui6
M. Ficky AfriantoAfiliasi tidak diketahui6
Mardian PeslinofAfiliasi tidak diketahui6
Frastica DeswardaniAfiliasi tidak diketahui5
Tika RestianingsihAfiliasi tidak diketahui5
Alrizal AlrizalAfiliasi tidak diketahui5
Maison MaisonAfiliasi tidak diketahui4
Ilham Jaya WindhanuAfiliasi tidak diketahui4
Sutrisno SutrisnoSekolah Tinggi Agama Buddha Negeri Raden Wijaya Wonogiri3
Madyawati LatiefAfiliasi tidak diketahui3
Ichy Lucya RestaAfiliasi tidak diketahui3
Fernando Mersa PutraAfiliasi tidak diketahui2
Arnild Augina MekarisceAfiliasi tidak diketahui2
Oka Lesmana SAfiliasi tidak diketahui2
Faizar FaridAfiliasi tidak diketahui2
Sampe NapitupuluAfiliasi tidak diketahui2
Sarinah PakpahanAfiliasi tidak diketahui2
Cindy Nur SyaqilaAfiliasi tidak diketahui2
Ardiyaningsih Puji LestariAfiliasi tidak diketahui2
Syamsuddin PasamaiUniversitas Muslim Indonesia1
Sri Lestari PoernomoUniversitas Muslim Indonesia1
Mursidin MursidinAfiliasi tidak diketahui1
Rifai NurAfiliasi tidak diketahui1
Tanzil TanzilUniversitas Halu Oleo1
Jamaluddin HosUniversitas Halu Oleo1
Fairuz Athaya zahran Tane TaneAfiliasi tidak diketahui1
Nurhidayah NurhidayahAfiliasi tidak diketahui1
M. Ficky ApriantoAfiliasi tidak diketahui1
Nurhidayah NurhidayahAfiliasi tidak diketahui1
Nurhidayah NurhidayahAfiliasi tidak diketahui1
Rista Mutia AnggrainiAfiliasi tidak diketahui1
Nurhidayah NurhidayahAfiliasi tidak diketahui1
Sherli YurinandaAfiliasi tidak diketahui1
Zuli RodhiyahAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://online-journal.unja.ac.id/index.php/jop/oai, pertama di-harvest 6 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">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>
</oai_dc:dc>