Article Per Year (5 Year)
p-Index From 2020 - 2025
1.679
P-Index
This Author published in this journals
All Journal Jurnal AGROTEKNOLOGI AUSTENIT Jurnal Keteknikan Pertanian Jurnal Dinamika Penelitian Industri agriTECH IJEMS (Indonesian Journal of Environmental Management and Sustainability) Seminar Nasional Lahan Suboptimal Dinasti International Journal of Education Management and Social Science PUBLIKASI PENELITIAN TERAPAN DAN KEBIJAKAN Prima Abdika: Jurnal Pengabdian Masyarakat Edible : Jedb SRIWIJAYA JOURNAL OF ENVIRONMENT Jurnal Polimesin
Amin Rejo
Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Keteknikan Pertanian

 1 
APLlKASI ARTIFICIAL NEURAL NETWORK UNTUK MENDUGA PRODUKSI TEBU (Saccharum officinarum L) DI PTPN VII PG. CINTA MANIS Amin Rejo
Jurnal Keteknikan Pertanian Vol. 21 No. 4 (2007): Jurnal Keteknikan Pertanian
Publisher : PERTETA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19028/jtep.021.4.%p

Abstract

AbstractThe objective of this research was to estimate the production of sugarcane by using artificial neural network algorithm. The training was done at soma variety of iteration, those are 1000, 2000, 3000, and 4000 with the nodes at the hidden layer. 2,4, 6, and 8. Based on the simulation. it showed that the more nodes and iteration could reduce sum of squere error. for example iteration 1000 at node 2, sum of square error was 0.0148. and at the node 8, sum of squares error was 0.000196 In ither side at the node 2, the iteration 1000, the sum of squares was 0.0148, and the iteration 4000, the sum of squares was 0.00345. The result of the model test showed that sugarcane productivity had the exact for prediction that was from 87. 5 % to 97.15 %, for sugarcane production it had low prediction that was 40.0 % to 92 5 .% and respectively, it had 94.2 % to 98.8 %. Keywords: Artificial neural network, beckpropaqation, estimate of suqaercene production Diterima: 18 Nopember 2007; Disetujui: 3 Desember 2007  
Co-Authors Abdul Rahman Adhiguna, Rizky Tirta Afrizal Vachlepi, Afrizal Andika, Eva Juniar Andries Lionardo, Andries Aulia, Nurul Izzah Azizah Husin Basuni Hamzah Dadang Hikmah Purnama, Dadang Hikmah Daniel Saputra Destri, Erin Dwi Budi Santoso Endo Argo Kuncoro Febrida, Cely Gatot Priyanto Gumay, Muhammad Hendri Handoko, Widi Haryani, Fatria Resti Hasbi Haskari, Farry Apriliano Indra Gunawan Laila Hanum Nutayla, Nazula Puspitahati Puspitahati - Rahmaniar ., Rahmaniar Rahmaniar Rahmaniar, Rahmaniar Railia Karneta Railia Karneta Railia Karneta Rindit Pambayun Rindit Pambayun Rindit Pambayun Robbani, Syifa Setyaaji, Primayoga Harsana Syapoetri, Dessy Tamaria Panggabean Wahab, harsyamsi Zainubbi, Zainubbi Zuhir, Mada Apriandi Zulyanti, Afriyani