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All Journal Analisis Kebijakan Pertanian Widyariset Widyariset MAHATANI: Jurnal Agribisnis (Agribusiness and Agricultural Economics Journal)
Harmi Andrianyta
Balai Besar Pengkajian dan Pengembangan Teknologi Pertanian
Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Neuroscience Physics Social Sciences

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Journal : Widyariset

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EFEKTIVITAS METODE NONDESTRUKTIF NIR-JARINGAN SARAF TIRUAN DALAM MENENTUKAN KOMPOSISI KIMIA JAGUNG Harmi Andrianyta; I Wayan Budiastra
Widyariset Vol 13, No 2 (2010): Widyariset
Publisher : Pusbindiklat - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (490.726 KB) | DOI: 10.14203/widyariset.13.2.2010.15-21

Abstract

High maize production should be followed by good handling and preservation up to consumer. Near Infrared Reflectance (NIR) was nondestructive testing method, as well as high accuracy, free from pollution, and rapid method, therefore suggested as a testing method. The objective of this study was asses of NIR technology efectivity in determining four major compositions of maize. Fifty samples of maize (intact seeds) were scanned from 900-2000 nm NIR wavelength, interval 5 nm. Calibration model for NIR measurement using Artificial Neural Network (ANN) technique three layers. As input layer ANN are 5, 10, and 15 nodes principal component (PC), hidden layer 4,6, 8, 10, and 12 nodes and output layer are single chemical composition and simultaneously. Prediction of an external validation set showed low the SEP (standard error of prediction) and CV (coeficient of variability). As result, NIR technology is able to predict maize chemical composition accurately SEP ranged from 0.004-0.496, CVranged from 0.047–0.518. ANN with 5 nodes input layer and single output layer were very strong recommended to generate NIR calibration model.
Co-Authors Hari Hermawan I Wayan Budiastra Rima Purnamayani