Outperforming DNN Using MLP in Water Quality Assessment for Aquaculture - Anshori, Mochammad; Musthofa, Mufid (Journal of Applied Informatics and Computing, 2026)
Aquaculture production relies heavily on stable water quality conditions, requiring accurate and efficient assessment methods to support early environmental monitoring and sustainable management. Although deep neural network models have been widely applied to water quality classification, their high computational complexity often limits their applicability in real-time and resource-constrained aquaculture systems. This study aims to evaluate whether a systematically optimized Multilayer Perceptron can outperform a reported deep neural network benchmark in aquaculture water quality assessment while maintaining computational efficiency. The study adopts a structured methodology involving dataset characterization, extreme outlier removal, feature normalization, and stratified data partitioning. A single-hidden-layer Multilayer Perceptron is trained using a feedforward backpropagation learning process, with systematic exploration of hidden neuron configurations and training epochs to identify the optimal architecture. Model performance is evaluated using multiple classification metrics, including accuracy, precision, recall, F1-score, confusion matrix analysis, and receiver operating characteristic and precision–recall curves. Results indicate that the optimal Multilayer Perceptron configuration, consisting of 80 hidden neurons and 200 training epochs, achieves an accuracy of 96.62%, surpassing the deep neural network benchmark accuracy of 95.69%. The proposed model demonstrates strong class-level performance, clear separation between water quality categories, stable convergence behavior, and reduced computational overhead compared to deeper architectures. These findings highlight that increasing model depth does not necessarily improve predictive performance for heterogeneous aquaculture datasets. In conclusion, this study provides empirical evidence that a well-optimized shallow neural network can outperform deeper models in aquaculture water quality assessment. The results emphasize the importance of model parsimony and systematic hyperparameter optimization, offering a practical and efficient solution for real-time aquaculture water quality monitoring applications.