Parameter Tuning in Backpropagation Neural Networks: Impact of Learning Rate and Momentum on Performance

Artificial Neural Network (ANN) play a pivotal role across diverse domains, including medicine, economics, and technology, due to their ability to model complex relationships and deliver high prediction accuracy. This study systematically examines how learning rate and momentum interact in backpropagation, moving beyond isolated analysis to enhance ANN performance. A qualitative research design employing a systematic literature review was utilized, with data sourced from reputable databases covering the past 11 years. Bibliometric tools such as VOSviewer and R-Studio were applied to identify trends and patterns in the literature. The findings reveal that both learning rate and momentum significantly impact convergence efficiency and model stability. Backpropagation remains fundamental for weight adjustment in minimizing prediction errors. ANN optimization demonstrates substantial practical benefits, including enhanced treatment outcome predictions in medicine, modeling nonlinear patterns in economics, and improved image classification accuracy. However, challenges such as the curse of dimensionality, overfitting, and dependence on large datasets persist. Strategies such as regularization, ensemble methods, and sensitivity analysis present viable solutions. This study underscores the critical need to advance ANN optimization techniques and highlights the potential of interdisciplinary collaboration in addressing existing limitations and broadening ANN applications