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I Wayan Budiastra
Departemen Teknik Mesin Dan Biosistem, IPB University
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 Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Neuroscience Physics Social Sciences Other

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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 . Saputera . Sutrisno A. Trisnobudi Agus A Munawar Ahmuhardi Abdul Azis Alla Asmara Amin Rejo Amoranto Trisnobudi Anggie Yulia Sari Aris Purwanto Aryanis Mutia Zahra Baihaqi Baihaqi Bambang Haryanto Bambang Haryanto Bambang Haryanto Bambang Haryanto BAMBANG S. PURWOKO Bambang S. Purwoko Bambang Soenarto Baskoro Wicaksono, Baskoro Deva Primadia Dheni Mita Mala Dwi Dian Novita Ei Mon Kyaw Elizabeth Sonya Lumbantoruan Emmy Darmawati Fadfhillah, Dini Nur Fadhillah, Dini Nur Fila Rodotul Jannah Fiona Hanberia Innayah Hadi K. Purwadaria Hadi K. Purwadaria Hadi K. Purwadaria Hafiz Fajrin Aditama Harmi Andrianyta Herna Permata Sari I Kadek Suardika I M. S. Wibawa I NYOMAN SUTARPA SUTAMA I Wayan Redana Ida Bagus Made Suryatika Indah Kurniasari Irawaty Irawaty Jajang Juansah Jajang Juansah Jati Sumarto Putro Jeris Habfrons Wiatri Djereng Ketut Agus Karmadi KUDANG B. SEMINAR La Rianda LADY C. E. CH. LENGKEY Lady Lengkey Lalu Hendri Setiawan Mar'atus Sholihah Mawiti Infantri Mawiti Infantri Yekti Ni Nyoman, Ratini Noneng Fahri Novi Andareswari Noviar Harun Nur Arifiya Ocarina, Estika Parida, Desca Putu Doddy Heka Ardana Rini Rosita Rizal Syarief Rizky Wiradinata Rokhani Hasbullah Ruri Wijayanti Samsudin Samsudin SEDARNAWATI YASNI Semin Semin Sigid Hariyadi Siti Djamila Siti Zulaikah Slamet Susanto Slamet Widodo Slamet Widodo Sobir Sobir Sri Agustina Sri Citra Yuliana Madi Sugiyono . Sugiyono Sugiyono Sukrisno Widyotomo Suroso . Suroso . Suroso Suroso Sut risno Sutrisno - Sutrisno . Sutrisno Mardjan Sutrisno Sutrisno Tarisa Fadillah Titis Priyowidodo Trisma Rezeki Zairisman Usman Ahmad Vita N. Lawalata Wayan Gede Suharta Wendianing Putri Luketsi Wiranda G. Piliang Yasin, Yasnita Yul Y Nazaruddin Yunisa Tri Suci