Perbandingan Model Estimasi Artificial Neural Network Optimasi Genetic Algorithm dan Regresi Linier Berganda

*Jimmy Saputra Sebayang -  Jurusan Komputasi Statistik, Sekolah Tinggi Ilmu Statistik, Indonesia
Budi Yuniarto -  Jurusan Komputasi Statistik, Sekolah Tinggi Ilmu Statistik, Indonesia
Published: 30 Jun 2017.
Open Access Copyright (c) 2017 MEDIA STATISTIKA
License URL: http://creativecommons.org/licenses/by-nc-sa/4.0/
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Abstract

Multiple Linear Regression is a statistical approach most commonly used in performing predictive data modeling. One of the methods that can be used in estimating the parameters of the model on Multiple Linear Regression is Ordinary Least Square. It has classical assumptions requirements and often the assumptions are not satisfied. Another method that can be used as an alternative data modeling is Artificial Neural Network. It is  a free-distribution estimator because there's no assumptions that have to be satisfied.  However, modeling data using ANN has some problems such as selection of network topology, learning parameters and weight initialization. Genetic Algorithm method can be used to solve those problems. A set of simulation data was generated to test the reliability of ANN-GA model compared to Multiple Linear Regression model. Model comparison experiments indicate that ANN-GA model are better than Multiple Linear Regression model for estimating simulation data both on the data training and data testing.

Keywords:

Neural Network, Genetic Algorithm, Ordinary Least Square

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