Comparative Study of NIR-Based Machine Learning for Predicting Soil Nutrients in Indonesian Farmlands
Authors
Adnan Adnan
National Research and Innovation Agency, Research Organisation for Energy and Manufacture, Research Centre for Sustainable Production System and Life Cycle Assessment, Tangerang Selatan, Indonesia
Taufik Iqbal Ramdhani
National Research and Innovation Agency, Research Organisation for Electronics and Informatics, Research Centre for Artificial Intelligence and Cyber Security, Bandung, Indonesia
Universitas Indonesia, Faculty of Engineering, Department of Electrical Engineering, Depok, Indonesia
Yaya Suryana
National Research and Innovation Agency, Research Organisation for Energy and Manufacture, Research Centre for Sustainable Production System and Life Cycle Assessment, Tangerang Selatan, Indonesia
Abdul Aziz
National Research and Innovation Agency, Research Organisation for Energy and Manufacture, Research Centre for Sustainable Production System and Life Cycle Assessment, Tangerang Selatan, Indonesia
Taslim Rochmadi
National Research and Innovation Agency, Research Organisation for Energy and Manufacture, Research Centre for Sustainable Production System and Life Cycle Assessment, Tangerang Selatan, Indonesia
Amrullah Kamaruddin
National Research and Innovation Agency, Research Organisation for Energy and Manufacture, Research Centre for Sustainable Production System and Life Cycle Assessment, Tangerang Selatan, Indonesia
Ninon Nurul Faiza
National Research and Innovation Agency, Centre for Data and Information, Jakarta, Indonesia
DOI: https://doi.org/10.2478/ata-2026-0004 | Journal eISSN: 1338-5267
Language: English
Page range: 27 - 34
Published on: Feb 9, 2026
Published by: Slovak University of Agriculture in Nitra
In partnership with: Paradigm Publishing Services
Keywords:
Related subjects:
© 2026 Adnan Adnan, Taufik Iqbal Ramdhani, Yaya Suryana, Abdul Aziz, Taslim Rochmadi, Amrullah Kamaruddin, Ninon Nurul Faiza, published by Slovak University of Agriculture in Nitra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.