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All Journal JURNAL ELEKTRO
Karel Octavianus Bachri
Graduate School Electrical Engineering, School of Bioscience, Technology, and Innovation, Atma Jaya Catholic University of Indonesia, Jakarta 12930, INDONESIA
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Analisis Performansi Jaringan Saraf Dalam terhadap Dataset Digit Berderau Karel Octavianus Bachri
Jurnal Elektro Vol 18 No 1 (2025): Jurnal Elektro: April 2025
Publisher : Program Studi Teknik Elektro, Fakultas Biosains, Teknologi, dan Inovasi, Universitas Katolik Indonesia Atma Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v18i1.6613

Abstract

This work investigates the impact of noise on model performance by training a neural network on a digit dataset with varying Signal-to-Noise Ratios (SNR) to assess its resilience and generalization ability. The experimental setup involved training the model on datasets with noise levels ranging from clean images to highly distorted ones (SNR 5%–25%), analyzing accuracy, mini-batch loss, and training time. Results indicate that while the model achieves high accuracy (96.88%) at mild noise levels (SNR 5%), performance declines significantly at higher noise levels, with accuracy dropping to 78.91% at SNR 25%. The analysis of mini-batch loss and training time reveals that noise slows convergence and increases computational complexity. The confusion matrix further confirms that while the model effectively distinguishes between classes, noise-induced misclassifications become more frequent at lower SNRs. These findings emphasize the importance of noise reduction techniques and data preprocessing to improve model robustness in real-world applications.
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