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All Journal JTSL (Jurnal Tanah dan Sumberdaya Lahan) Advance Sustainable Science, Engineering and Technology (ASSET)
Lenny Sri Nopriani
Universitas Brawijaya
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering

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ANALISIS SIFAT KIMIA GAMBUT PASCA KEBAKARAN DENGAN BERBAGAI UPAYA PEMULIHAN HUTAN DI KAWASAN HUTAN DENGAN TUJUAN KHUSUS (KHDTK) TUMBANG NUSA, KALIMANTAN TENGAH Fytria Kurniasari; Syahrul Kurniawan; Lenny Sri Nopriani; Dony Rachmanadi
Jurnal Tanah dan Sumberdaya Lahan Vol. 8 No. 1 (2021)
Publisher : Departemen Tanah, Fakultas Pertanian, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (327.401 KB) | DOI: 10.21776/ub.jtsl.2021.008.1.25

Abstract

Peat land has an important role, function, and benefit for human life, biodiversity, and global climate. The peat swamp forest in Central Kalimantan was devastated to a very large extent, which addresses the restoration of peat swamp forests, has changed the characteristics of peat which contain physical, chemical, and biological characteristics. Peat recovery efforts carried out at the KHDTK Tumbang Nusa consist of natural succession and revegetation. The study aimed to analyze the chemical properties of peat soils in different types of post-fire forest protection and to assess the spatial variability of peat chemical properties in Forest Areas for Special Purposes (KHDTK) Tumbang Nusa, Desa Tumbang Nusa, Jabiren Raya District, Pulang Pisau Regency. The research plot consisted of forest restoration efforts, namely secondary forest, revegetation, and natural regeneration. Variable measured consisted of pH, total organic C, CEC, N, P, K, exchangeable K, Na, Ca, Mg, as well as ash content and water content. The results showed that secondary forest treatment was the best peat swamp forest restoration technique. Spatial variability sub-plots and sample points did not significantly affect the differences in chemical properties of the peat, but the differences in chemical properties of peat is the peat swamp forest was determined by restoration technique.
Prediction of Soil Nutrients from Different Soil Textures using Portable Spectrometer and Machine Learning Harki Himawan; Rut Juniar Nainggolan; Handono Rakhmadi; Gunomo Djoyowasito; Ubaidillah; Lenny Sri Nopriani; Dimas Firmanda Al Riza
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2166

Abstract

Soil nutrients, such as nitrogen, phosphorus, and potassium, are critical for plant growth and agricultural productivity. Conventional laboratory methods for measuring these nutrients are accurate but often time-consuming, costly, and environmentally taxing. This study explores the potential of portable visible-near infrared (Vis-NIR) spectrometer combined with machine learning algorithms as a rapid, cost-effective, and eco-friendly alternative for soil nutrient analysis. Soil samples of clay, clay loam, and sandy clay were collected and analyzed using artificial neural network (ANN) approach to predict soil nutrients. A total of 81 reflectance spectra data from each soil type were acquired using an AS7265x sensor and processed to develop a predictive model for nutrient content. ANN models demonstrated high accuracy, with R² values exceeding 0.8 in each type of soil texture. This study emphasizes the potential of portable Vis-NIR spectrometer and machine learning integration to revolutionize soil nutrient analysis, offering significant improvements in agricultural efficiency and sustainability.
Co-Authors Dimas Firmanda Al Riza Dony Rachmanadi Fytria Kurniasari Gunomo Djoyowasito Handono Rakhmadi Harki Himawan Rut Juniar Nainggolan Syahrul Kurniawan Ubaidillah