A New Hybrid Grasshopper Optimization - Backpropagation for Feedforward Neural Network Training

Alsammarraie, Samer; Hussein, Nazar K.

doi:10.25130/j.v25i1.944

Cited by 4 publications

(3 citation statements)

References 41 publications

(55 reference statements)

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“…Metode backpropagation merupakan metode berbasis gradien yang banyak digunakan untuk perlatihan jaringan saraf feedforward (umpan maju) [17][18][19]. Perkembangan metode backpropagation dengan beberapa fungsi, diantaranya fungsi transfer dan perlatihan yang masing-masing fungsi juga dengan banyak teknik dan metode yang dapat digunakan untuk menyelesaikan penyelesaian masalah dengan tingkat kekompleksan yang tinggi.…”

Section: Pendahuluanunclassified

Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana

Wanto

Defit

Windarto

2021

RESTI

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Research has been carried out with several training functions using standard backpropagation methods, One-Step Secant (OSS), and Bayesian regulation. The purpose of this study was to (i) analyze the Performance accuracy (Performance) of the standard backpropagation method and (ii) optimize the training function with the One-Step Secant (OSS) and Bayesian regulation methods to obtain comparison results of the three methods in the search for the best results implementation of disaster phenomenon forecasting data. The research method is based on quantitative methods with times-series data on disaster phenomena in Indonesia over the last ten years (2011-2020) which were analyzed using two network architecture models, namely 4-8-1 and 4-10-1. The results showed that the 4-8-1 architectural model with the Bayesian regulation training function method was able to optimize quite well through accelerating training time and resulted in a low MSE measurement, although not the lowest with an epoch value of 197 iterations and a Performance of 0.0148480766. The lowest epoch value is generated by the OSS method, but it Performs poorly. The best Performance is produced by the standard backpropagation method with the traingd training function, but the training process for achieving convergence is also too long. In general, it can be concluded that the 4-8-1 architectural model with Bayesian regulation can be used to predict (predict) the phenomenon of natural disasters in Indonesia because the training time to achieve convergence is not too long and Performs exceptionally well.

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Section: Pendahuluanunclassified

Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana

Wanto

Defit

Windarto

2021

RESTI

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“…Back-propagation is widely used for training feedforward neural networks which are gradient-based algorithms [6][7][8]. Based on its development, Back-propagation has different functions, including the activation function (transfer) and the training function, each of which has many methods and techniques that can be used to solve complex computational problems.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Performance Traditional Back-propagation with Cyclical Rule for Forecasting Model

Wanto,

Ginantra,

Hendraputra

et al. 2022

matrik

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The traditional Back-propagation algorithm has several weaknesses, including long training times and significant iterations to achieve convergence. This study aims to optimize traditional Back-propagation using the cyclical rule method to cover these weaknesses. Optimization is done by changing the training function and standard Back-propagation parameters using the training function and cyclical rule parameters. After that, a comparison of the two results will be carried out. This study uses quantitative method of time-series data on coronavirus cases sourced from the Worldometer website, then analyzed using three forecasting models with five input layers, one hidden layer (5, 10, and 15 neurons) and one output layer. The results showed that the 5-10-1 model with the training function and cyclical rule parameters and the tansig and purelin activation functions could perform well in optimization, including faster training time and smaller iterations (epochs), MSE training performance, and better tests. Low and high accuracy (92%) with an error rate of 0.01. So it was concluded that the training function and cyclical rule parameters with the tansig and purelin activation functions were able to optimize the traditional Back-propagation method, and the 5-10-1 model could be used for forecasting active cases of the coronavirus in Asia

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“…[5] Teknik Machine Learning bertujuan agar komputer dapat belajar secara otomatis tanpa adanya campur tangan manusia, [6] serta menyesuaikan tindakan yang tepat. [7] Fokus penelitian Machine Learning tentang bagaimana meningkatkan kinerja sistem pembelajaran otomatis melalui pelatihan-pelatihan atau pengalaman. [8] permasalahan Machine Learning digunakan sebagai pilihan dalam mengoptimalkan cara kerja komputer sesuai dengan data lalu.…”

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Analisis Optimasi Fungsi Pelatihan Machine Learning Neural Network dalam Peramalan Kemiskinan

Sitanggang¹,

Defit

Ramadhan³

2021

JEPIN

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Banyak metode fungsi pelatihan dalam Machine Learning Neural Network yang digunakan dalam menyelesaikan masalah komputasi yang berkaitan dengan prediksi. Fungsi pelatihan yang digunakan pada Machine Learning metoda algoritma backpropagation dapat menghasilkan prediksi yang berbeda, yang dipengaruhi oleh parameter dan data yang digunakan. Tujuan dari penelitian dilakukan untuk menganalisa performance dan keakuratan algoritma backpropagation standard serta mengoptimalkan fungsi pelatihan dengan algoritma Bayesian Regulation, dan One Step Secant. Dalam proses analisis, penelitian ini menggunakan Dataset jumlah kemiskinan di Indonesia dalam jangka waktu 12 tahun (tahun 2009 - 2020) yang terdiri dari 34 provinsi. Data diperoleh dari website Badan Pusat Statistik (BPS) Indonesia https://www.bps.go.id/. Berdasarkan pelatihan, pengujian, dan analisa yang dilakukan diperoleh hasil dari penelitian, bahwa model jaringan 5-9-1 menggunakan fungsi pelatihan Bayesian Regulation mampu melakukan optimasi yang lebih baik dengan percepatan waktu pelatihan, MSE Pengujian, Performance lebih rendah dibandingkan denga 2 metode yang lain, dengan demikian disimpulkan bahwa model jaringan 5-9-1 menggunakan algoritma Bayesian Regulation dapat digunakan untuk prediksi kemiskinan di Indonesia.

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A New Hybrid Grasshopper Optimization - Backpropagation for Feedforward Neural Network Training

Cited by 4 publications

References 41 publications

Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana

Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana

Optimization of Performance Traditional Back-propagation with Cyclical Rule for Forecasting Model

Analisis Optimasi Fungsi Pelatihan Machine Learning Neural Network dalam Peramalan Kemiskinan

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