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Ony Ramadhan Armanto
Universitas Teknologi Surabaya
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering

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Analisa Performa Metode Training Artificial Neural Network dalam Mendeteksi Total Harmonic Distortion Masyitah Aulia; Ony Ramadhan Armanto; Moh.Yasya Bahrul Ulum
Hexagon Vol 6 No 2 (2025): Vol. 6 No. 2 (2025): HEXAGON - Edisi 12
Publisher : Fakultas Teknologi Lingkungan dan Mineral - Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36761/hexagon.v6i2.6283

Abstract

This Study aims to asses the performance of several training algorithms in Artificial Neural Network for pattern classification task based on input data. The research problem focuses on how training algorithms can produce low error rates in classification. The scientific gap lies in the limited comparative studies that comprehenseively exmine various training methods with variations in the number of hidden neurons. This research utilizes five training methods, Levenburgh – Marquadt, Scaled Conjugate Gradient, Resilient Backpropagation, BFGS Quasi – Newton, and Bayesian Reqularization. The hidden neuron variations range from 5 to 50 neurons. The result indicate that the best performance is achieved using the Bayessian Regularization method, which produces a MAPE value 0.01, an RMSE of 0.0167, abnd accuracy of 99.98%. These findings demonstrate that choosing the appropriate training function significantly affects the performance of Artificial Neural Networks.
Co-Authors Masyitah Aulia Moh.Yasya Bahrul Ulum