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All Journal JURIKOM (Jurnal Riset Komputer)
Irnanda, Khairunnissa Fanny
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Optimasi Particle Swarm Optimization Pada Peningkatan Prediksi dengan Metode Backpropagation Menggunakan Software RapidMiner Irnanda, Khairunnissa Fanny; Windarto, Agus Perdana; Damanik, Irfan Sudahri
JURIKOM (Jurnal Riset Komputer) Vol 9, No 1 (2022): Februari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i1.3836

Abstract

Backpropagation is a method of Artificial Neural Networks that is quite reliable in solving prediction problems (forecasting). However, in its application, this algorithm still has weaknesses such as optimizing the artificial neural network weights to avoid local minimums, the problem of long training times to achieve convergence and the process of determining the right parameters (learning rate and momentum) in the training process. The purpose of this research is to solve this problem by using Particle Swarm Optimization (PSO) which is a simple and reliable optimization algorithm to solve optimization problems. The data source is obtained from the site sumut.bps.go.id. There are 5 network architecture models used in this study, including 2-5-1, 2-7-1, 2-9-1, 2-11-1 and 2-13-1. The results of trials conducted with Rapid Miner software, the best architectural model is the 2-9-1 model with a total RMSE of 0.056 +/- 0.000 in the implementation of Backpropagation, while in the implementation of Backpropagation + particle swarm optimization the amount of RMSE is 0.055 +/- 0.000. The smaller the RMSE (Root Mean Squared Error), the better the model
Co-Authors Agus Perdana Windarto Irfan Sudahri Damanik