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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Lailia Awalushaumi
Dept. of Mathematics, Faculty of Mathematics and Natural Sciences, University of Mataram
Computer Science & IT

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Enhanced Performance of Dynamic Neural Network Model using Wavelet Activation Functions Syamsul Bahri; Lailia Awalushaumi; Nurul Fitriyani
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 14 No 3 (2023): Vol. 14, No. 3 December 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i03.p03

Abstract

Both static and dynamic adaptive neural networks have been broadly utilized in mathematical modeling and numerical analysis. This study aimed to enhance the accomplishment of Dynamic Neural Networks (DNN) models by applying wavelet functions as activation functions. Research that models and forecasts the intensity of solar radiation in Mataram City shows that combining B-Spline and Morlet wavelet activation functions can significantly increase the DNN model performance. Wavelet-DNN (W-DNN) was modeled with an identical architecture; the best showed the increase in the model achievement (0.7596 points for in-sample and 0.8502 points for out-sample data). Mainly for out-sample data, the model's performance using the W-DNN+ intervention model increased by 4.0492 points.
Co-Authors Nurul Fitriyani Syamsul Bahri