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ITS » PhD Theses » Program Doktoral Teknik Elektro
Posted by budi_hrt@its.ac.id at 22/03/2022 13:21:27  •  92 Views


ARTIFICIAL LIFE OF PLANT GROWTH MODELING ON SOYBEANS PLANT USING INTELLIGENCE APPROACHES

Author :
Atris Suyantohadi  ( 2207301710 )




ABSTRAK

Sistem produksi pertanian seperti pada pertumbuhan tanaman kedelai memiliki karateristik sifat yang komplek dan kondisi yang tidak pasti. Dalam penelitian ini pemodelan sistem dinamis pada pertumbuhan tanaman kedelai untuk identifikasi diameter batang dan indek panjang daun total tanaman diperoleh menggunakan pendekatan intelligence. Nilai optimal diameter batang dan indek panjang daun total pertumbuhan tanaman kedelai yang dipengaruhi oleh komposisi pupuk nitrogen menggunakan metoda neural network dan genetic algorithm dan prototipe dasar model visualisasi pertumbuhan tanaman menggunakan metoda Lindenmayer Sistem diperoleh dari penelitian ini. Perubahan dinamis pertumbuhan tanaman kedelai pada diameter batang dan indek panjang daun total diidentifikasi pada awal model menggunakan neural network dan nilai optimal diperoleh melalui simulasi hasil identifikasi model neural network menggunakan genetic algorithm. Metoda L-system memvisualisasikan pertumbuhan tanaman dalam lingkup grafis berdasarkan aturan grammer pertumbuan tanaman. Penelitian ini memberikan hasil temuan baru dalam pemodelan sistem dinamis pertumbuhan tanaman kedelai dalam pencapaian optimasi dan visualisasi grafis yang tersusun atas metoda neural network genetic algorithm dan Lindenmayer System. Nilai optimal pada hasil akhir pada pengukuran 4 tahapan pertumbuhan tanaman kedelai pada umur 30 hari dihasilkan rasio diameter batang dan indek panjang daun total sebesar 0.31. Komposisi pupuk nitrogen yang digunakan adalah sebesar 75kgha pada umur 0 hari 75 kgha pada umur 20 hari 50 kgha pada umur 20 hari dan 50 kgha pada umur 30 hari. Validasi model cross-validation dicapai pada nilai galat minimum 0.1. Hasil akhir penelitian diberikan bahwa pemodelan sistem dinamis menggunakan neural network dan genetic algorithm dapat diterapkan untuk pencarian nilai optimal optimalisasi pada pertumbuhan tanaman kedelai dan pemodelan visualisasi pertumbuhan tanaman menggunakan L-System. Hasil akhir memberikan koreksi untuk pengembangan teknologi yang dapat diterapkan dibidang pertanian.


ABSTRACT

Agricultural production system such as soybean plant growth are characterized by complexity and unvertanty condition. In this study dynamic system modeling on soybean plant growth of stem diameter and index of total leaf length was investigated using intelligence approaches. An optimal stem diameter and index of total leaf length on soybean plant growth that its have been influenced nitrogen fertilizer composition using neural network and genetic algorithm and fundamental prototype of visualization on plant growth using Lindenmayer System were investigated. Dynamic change of soybean plant growth on stem diameter and index of total leaf length were first identified using neural network and then an optimal value was determined through simulation of the identified neural network model using genetic algorithm. L-system method visualized plant growth in graphical environment on grammer rules of it. This study investigated a new resulted finding on dynamic system modeling for optimizing and visualizing on soybean plant growth with integrating using neural network genetic algorithm and Lindenmayer System. The final of optimal value on 4-step for growing soybean plant under 30 days resulted 0.31 on ratio of stem diameter and index of total leaf length. The nitrogen composed on 75kgha at 0 days 75 kgha at 10 days 50 kgjha at 20days and 50 kgha at 30 days. The cross-validation model achieved 0.1 for minimum error value. These results suggest that dynamic system modeling using neural network and genetic algorithm could be introduced for searching an optimal value on soybean plant growth and and visualization modeling using L-System. The final result has been correction for developing technology on agricultural fields.



KeywordsPendekatan intelligence; pertumbuhan tanaman; kedelai; neural network; genetic algorithm; L-System
 
Subject:  Permainan komputer
Contributor
  1. Prof. Ir. Mauridhi Hery Purnomo, M.Eng., Ph.D
  2. Mochamad Hariadi, S.T., M.Eng., Ph.D.
Date Create: 22/02/2011
Type: Text
Language: Indonesian
Identifier: ITS-PhD-3100011042897
Collection ID: 3100011042897
Call Number: RDE 006.3 Suy a


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