Outperforming DNN Using MLP in Water Quality Assessment for Aquaculture
DOI:
https://doi.org/10.30871/jaic.v10i1.11835Keywords:
Aquaculture, Backpropagation, MLP, Multilayer Perceptron, Neural NetworkAbstract
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.
Downloads
References
[1] N. Luvhimbi, T. G. Tshitangano, J. T. Mabunda, F. C. Olaniyi, and J. N. Edokpayi, “Water quality assessment and evaluation of human health risk of drinking water from source to point of use at Thulamela municipality , Limpopo Province,” Sci Rep, no. 0123456789, pp. 1–17, 2022, doi: 10.1038/s41598-022-10092-4.
[2] P. Ghosh and T. Chakravarty, “Sustainable Development Goals: Challenges, Opportunities, and the Way Forward,” in Sustainability: Science, Policy, and Practice in India: Challenges and Opportunities, V. Dutta and P. Ghosh, Eds., Cham: Springer International Publishing, 2023, pp. 235–241. doi: 10.1007/978-3-031-50132-6_17.
[3] B. L. Widiyanti, “Assessment of water quality and its effect on the health of Selong District Resident, East Lombok, West Nusa Tenggara Province,” IOP Conf Ser Earth Environ Sci, vol. 986, no. 1, p. 12078, Feb. 2022, doi: 10.1088/1755-1315/986/1/012078.
[4] I. Effendi, “Pengembangan Akuakultur pada Lahan Suboptimal Menuju,” Prosiding Seminar Nasional Lahan Suboptimal, vol. 7, no. 1, pp. 978–979, 2019.
[5] R. Nawaz et al., “Water Quality Index and Human Health Risk Assessment of Drinking Water in Selected Urban Areas of a Mega City,” 2023.
[6] A. Mohammadpour, E. Gharehchahi, and M. Amiri, “Assessment of drinking water quality and identifying pollution sources in a chromite mining region,” J Hazard Mater, vol. 480, no. June, p. 136050, 2024, doi: 10.1016/j.jhazmat.2024.136050.
[7] A. Al, M. Hridoy, C. Bordin, A. Masood, and K. Masood, “Results in Chemistry Predictive modelling of aquaculture water quality using IoT and advanced machine learning algorithms,” Results Chem, vol. 16, no. June, p. 102456, 2025, doi: 10.1016/j.rechem.2025.102456.
[8] R. Baena-navarro, Y. Carriazo-regino, F. Torres-hoyos, and J. Pinedo-l, “Intelligent Prediction and Continuous Monitoring of Water Quality in Aquaculture : Integration of Machine Learning and Internet of Things for Sustainable Management,” 2025.
[9] N. A. Regency, R. Ezraneti, and M. Khalil, “Study of Water Quality for Aquaculture System in Ujung Pacu River, North Aceh Regency,” Omni-Akuatika, vol. 15, no. 3, pp. 119–127, 2020, doi: http://dx.doi.org/10.20884/1.oa.2020.16.3.849.
[10] R. P. Wisnu, M. Karuniasa, and S. S. Moersidik, “The effect of fish aquaculture on water quality in Lake Cilala, Bogor Regency,” IOP Conf Ser Earth Environ Sci, vol. 399, no. 1, p. 12111, Dec. 2019, doi: 10.1088/1755-1315/399/1/012111.
[11] S. M. Naim, P. Das, and J. Tiang, “Aquaculture Water Quality Classification Using XGBoost Classifier Model Optimized by the Honey Badger Algorithm with SHAP and DiCE-Based Explanations,” pp. 1–18, 2025.
[12] R. Arabelli, T. Bernatin, and V. Veeramsetty, “Water quality assessment for aquaculture using deep neural network,” Desalination Water Treat, vol. 321, no. October 2024, p. 101016, 2025, doi: 10.1016/j.dwt.2025.101016.
[13] A. Lazcano and M. A. Jaramillo-mor, “Back to Basics : The Power of the Multilayer Perceptron in Financial Time Series Forecasting,” Mathematics, vol. 12, no. 12, pp. 1–18, 2024, doi: 10.3390/math12121920.
[14] Enung, H. Kasyanto, and R. R. Sari, “Penerapan Algoritma Multilayer Perceptron (MLP) Untuk Memprediksi Debit Di Sungai Citarum Bagian Hulu (Pos Pengukuran Majalaya), Kab.Bandung, Jawa Barat,” Potensi: Jurnal SIpil Politeknik, vol. 25, no. 1, pp. 1–8, 2023, doi: https://doi.org/10.35313/potensi.v25i1.4513.
[15] A. D. Novika and A. S. Girsang, “Multi-layer perceptron hyperparameter optimization using Jaya algorithm for disease classification,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 35, no. 1, pp. 620–630, 2024, doi: 10.11591/ijeecs.v35.i1.pp620-630.
[16] K. Amadea, F. A. Bachtiar, and G. Pangestu, “Klasifikasi Pola Pergerakan Bola Mata Menggunakan Metode Multilayer Backpropagation,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 9, no. 2, pp. 391–400, 2022, doi: 10.25126/jtiik.202295668.
[17] M. Anshori, M. S. Haris, and W. Teja Kusuma, “Penerapan Backpropagation Neural Network (BPNN) Untuk Prediksi Kecanduan Smartphone Pada Remaja,” Cices, vol. 9, no. 2, pp. 192–202, 2023, doi: 10.33050/cices.v9i2.2701.
[18] N. Nasir et al., “Water quality classification using machine learning algorithms,” Journal of Water Process Engineering, vol. 48, p. 102920, 2022, doi: https://doi.org/10.1016/j.jwpe.2022.102920.
[19] U. Machine and L. Techniques, “Prediction of Water Quality Classification of the Kelantan River Basin , Malaysia , Using Machine Learning Techniques,” Water (Basel), vol. 14, no. 7, 2022, doi: 10.3390/w14071067.
[20] J. Yung et al., “Towards a time and cost effective approach to water quality index class prediction,” J Hydrol (Amst), vol. 575, no. March, pp. 148–165, 2019, doi: 10.1016/j.jhydrol.2019.05.016.
[21] C. Sanjeev, K. Dash, A. Kumar, S. Dehuri, and A. Ghosh, “An outliers detection and elimination framework in classification task of data mining,” Decision Analytics Journal, vol. 6, no. January, p. 100164, 2023, doi: 10.1016/j.dajour.2023.100164.
[22] O. Colliot, A Non-technical Introduction to Machine Learning, vol. 197. Paris: Humana Press, 2023. doi: 10.1007/978-1-0716-3195-9_1.
[23] F. Farias, T. B. Ludermir, and C. Bastos-filho, “Similarity Based Stratified Splitting : an approach to train better classifiers,” no. November, 2020, doi: 10.48550/arXiv.2010.06099.
[24] A. Bhaskara, A. A. Ginting, and A. M. Masagala, “Penerapan Konstruksi Ramping terhadap Waste pada Ruang Lingkup Manajemen Proyek (Studi Kasus: Proyek Pembangunan Gedung Auditorium di Yogyakarta),” Semesta Teknika, vol. 25, no. 1, pp. 80–88, 2022, doi: 10.18196/st.v25i1.13403.
[25] F. N. Arifah et al., Machine Learning: Seni dan Sains Belajar dari Data. Yayasan Kita Menulis, 2025.
[26] M. Li, “Comprehensive Review of Backpropagation Neural Networks,” Academic Journal of Science and Technology, vol. 9, no. 1, 2024, doi: https://doi.org/10.54097/51y16r47.
[27] S. H. Gulo, A. H. Lubis, T. Informatika, F. Teknik, and U. M. Area, “Penerapan Multi-Layer Perceptron untuk Mengklasifikasi Penduduk Kurang Mampu,” EXPLORER Journal of Computer Science and Information Technology, vol. 4, no. 2, pp. 51–59, 2024, doi: 10.47065/explorer.v4i2.1146.
[28] L. Yang, “Theoretical Analysis of Adam Optimizer in the Presence of Gradient Skewness,” International Journal of Applied Science, vol. 7, no. 2, pp. 27–42, 2024, doi: https://doi.org/10.30560/ijas.v7n2p27.
[29] M. Musthofa and M. Anshori, “Comparing Discriminant Analysis Function for Early Prediction of Smartphone Addiction,” Journal of Enhanced Studies in Informatics and Computer Applications, vol. 2, no. 1, pp. 1–7, 2025, doi: https://doi.org/10.47794/jesica.v2i1.12.
[30] M. Anshori, W. T. Kusuma, and R. S. Pradini, “Prediction of Sleep Disorder : Insomnia Using AdaBoost Ensemble Learning Algorithm with Grid Search Optimization,” InComTech: Jurnal Telekomunikasi dan Komputer, vol. 14, no. 1, pp. 30–38, 2024, doi: 10.22441/incomtech.v14i1.19306.
[31] Muttaqin et al., Pengenalan Data Mining, First Edit., no. July. Yayasan Kita Menulis, 2023.
[32] M. C. Saletia, M. Anshori, and S. Haris, “Comparing Different KNN Parameters Based on Woman Risk Factors to Predict the Cervical Cancer,” Journal of Applied Informatics and Computing (JAIC), vol. 9, no. 5, p. 2179, Oct. 2025, doi: https://doi.org/10.30871/jaic.v9i5.10746.
[33] J. Cook, “When to consult precision-recall curves,” Stata J, vol. 20, no. 1, pp. 131–148, 2020, doi: 10.1177/1536867X20909693.
[34] M. Anshori and M. S. Haris, “Predicting Heart Disease using Logistic Regression,” Knowledge Engineering and Data Science, vol. 5, no. 2, p. 188, 2022, doi: 10.17977/um018v5i22022p188-196.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Mochammad Anshori, Mufid Musthofa
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
