Prediksi Wave-induced Liquefaction dengan Artificial Neural Network dan Wide Genetic Algorithm

Kristianto, Dwi (2017) Prediksi Wave-induced Liquefaction dengan Artificial Neural Network dan Wide Genetic Algorithm. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Fenomena Liquefaction telah menjadi perhatian peneliti sejak era 1970an, mulai dari
pemodelan analitik, pemodelan dengan Finite Element Method (FEM) sampai beragamnya
jenis pengujian di laboratorium. Salah satu bidang dalam penelitian liquefaction adalah
memprediksi lokasi kedalaman tanah tempat terjadinya liquefaction. Kerumitan pemodelan
analitik, kebutuhan untuk melakukan validasi laboratorium dan inspeksi lapangan, telah
membuka peluang untuk pengembangan pemodelan yang mudah, praktis, murah sekaligus
valid.
Dalam penelitian ini, pemodelan Artificial Neural Network (ANN) digunakan untuk
memprediksi kedalaman maksimum liquefaction. ANN dipilih karena ANN mampu
memodelkan interaksi komputasi paralel pada otak melalui proses learning terhadap data.
Metode Back Propagation (BP) adalah metode pelatihan yang paling banyak digunakan,
walaupun memiliki kelemahan mudah terjebak dalam local optimum dan tidak stabil. Untuk
mengatasi masalah tersebut, metode optimasi Genetic Algorithm (GA) digunakan dalam
proses pelatihan ANN. GA adalah metode optimasi yang menirukan proses evolusi, seleksi,
rekombinasi dan mutasi yang terjadi di alam.
Walaupun telah banyak digunakan, GA memiliki beberapa kelemahan yaitu
premature convergence dan local optimum. Dalam penelitian ini dilakukan modifikasi
terhadap GA, antara lain: operasi seleksi Wide Tournament, operasi rekombinasi BLX-α
Multi-Parent, operasi mutasi Aggregate Mate Pool dan operasi Direct Mutation-
Recombination. Modifikasi GA bertujuan untuk meningkatkan keragaman populasi,
memperluas cakupan pencarian solusi dan menemukan global optimum lebih mudah dan
cepat. Global optimum GA adalah konfigurasi ANN terbaik dengan error prediksi MdAPE
terkecil.

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Liquefaction phenomenon has been the attention of researchers since the 1970s, ranging
from analytical modeling, Finite Element Method (FEM) modeling to various types
laboratory testing. One of its research field is to predict the depth where soil liquefaction
occurred. The hassle of analytic modeling, repetitive laboratory testing and expensive field
inspections, has opened up opportunities to develop simple, practical, inexpensive and valid
modeling.
In this research, Artificial Neural Network (ANN) regression modeling is used to predict the
maximum depth of liquefaction. ANN selected among other Artificial Intelligence methods
because its ability to model the interaction of brain parallel computing through iterative
learning process of inputted data. Back Propagation (BP) is one of widely used training
method for ANN. Although it has some weaknesses such as easily trapped in local optimum
and unstable results, it is most widely used one. To overcome BP weaknesses, Genetic
Algorithm (GA) is being used for ANN learning process. GA is optimization method that
mimics the process of evolution, selection, recombination and mutation that occurs in nature.
Although GA is a well known method, it also has some weaknesses, such as premature
convergence and local optimum. In this research, GA is heavily modified to overcome its
weaknesses. The modifications are Wide Tournament selection, Multi-Parent BLX-α
recombination, Aggregate mutation and Direct Mutation-Recombination operation. The
modifications aim to increase the population diversity, expanding the coverage area of
solution search and find global optimum solution more easily and quickly. Global optimum
solution of GA is best ANN configuration which has smallest MdAPE prediction errors.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Wave-induced Liquefaction; Algoritma Genetika Lebar; Jaringan Saraf Tiruan; seleksi Wide Tournament; rekombinasi BLX-α Multi Induk; mutasi Aggregate Mate Pool; Direct Mutation-Recombination; Artificial Neural Network; Wide Genetic Algorithm; Wide Tournament selection; BLX-α Multi-Parent recombination; Aggregate mutation
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Divisions:	Faculty of Information Technology > Information System > 59101-(S2) Master Thesis
Depositing User:	DWI KRISTIANTO
Date Deposited:	31 Mar 2017 04:23
Last Modified:	22 Dec 2017 01:19
URI:	http://repository.its.ac.id/id/eprint/2664

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