Implementasi One-Dimensional Convolutional Neural Network untuk Klasifikasi Kualitas Air Depot Reverse Osmosis secara Real-Time berbasis Internet of Things

Filemon V Makaenas; Wayan G Y Sukarya; Toban T Pairunan; Oldi M Lambonan

doi:10.47065/josh.v7i4.10712

Filemon V Makaenas * Politeknik Negeri Manado, Manado, Indonesia
Wayan G Y Sukarya Politeknik Negeri Manado, Manado, Indonesia
Toban T Pairunan Politeknik Negeri Manado, Manado, Indonesia
Oldi M Lambonan Politeknik Negeri Manado, Manado, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/josh.v7i4.10712

Keywords: 1D-CNN; Water Quality Classification; Reverse Osmosis; Internet of Things; Deep Learning

Abstract

Water quality monitoring in refill reverse osmosis (RO)-based drinking water depots in Indonesia requires continuous surveillance because membrane performance degradation due to fouling can occur gradually without visual indication, potentially endangering consumer health. This study aims to implement and evaluate a One-Dimensional Convolutional Neural Network (1D-CNN) architecture for real-time water quality classification on an IoT-equipped RO filtration system at Politeknik Negeri Manado. The system simultaneously reads eight sensor parameters at two measurement points (inlet and outlet), comprising turbidity, pH, Total Dissolved Solids (TDS), and temperature. A synthetic dataset of 5,000 samples representing operational condition variations is classified into three classes (Normal, Warning, Danger) based on thresholds from Indonesian Ministry of Health Regulation No. 2/2023. Preprocessing includes Min-Max Scaling and a sliding window technique (size 10 timesteps) to construct three-dimensional input tensors. A compact 1D-CNN model with only ~3,000 parameters is trained using the Adam optimizer with early stopping to prevent overfitting. The main contributions include a Dual-Protection mechanism integrating deterministic regulation-based rules with CNN inference, and an Explainability Engine generating textual diagnostics in Indonesian for field operators. Evaluation results demonstrate 99.80% accuracy with only 1 misclassifications from 500 actual test samples, proving the proposed approach effective for automated, accurate, and interpretable real-time water quality monitoring in RO depots.

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References

M. A. Ahmed, S. Amin, and A. A. Mohamed, “Fouling in reverse osmosis membranes: monitoring, characterization, mitigation strategies and future directions,” Apr. 01, 2023, Elsevier Ltd. doi: 10.1016/j.heliyon.2023.e14908.

S. Senhaji, M. Hamlich, and M. Ouazzani Jamil, “Real time monitoring of water Quality using IoT and Deep learning,” in E3S Web of Conferences, EDP Sciences, Sep. 2021. doi: 10.1051/e3sconf/202129701059.

W. H. Sugiharto, H. Susanto, and A. B. Prasetijo, “Real-Time Water Quality Assessment via IoT: Monitoring pH, TDS, Temperature, and Turbidity,” Ingenierie des Systemes d’Information, vol. 28, no. 4, pp. 823–831, Aug. 2023, doi: 10.18280/isi.280403.

E. El-Shafeiy, M. Alsabaan, M. I. Ibrahem, and H. Elwahsh, “Real-Time Anomaly Detection for Water Quality Sensor Monitoring Based on Multivariate Deep Learning Technique,” Sensors (Basel), vol. 23, no. 20, Oct. 2023, doi: 10.3390/s23208613.

C. Tsolaki, G. Kokkonis, S. Valsamidis, and S. Kontogiannis, “Water Quality Identification: Integrating IoT Sensors and Deep Learning for Near-Real-Time Water Quality Assessment,” Applied Sciences (Switzerland), vol. 16, no. 10, May 2026, doi: 10.3390/app16104868.

K. Lal, S. Menon, F. Noble, and K. M. Arif, “Low-cost IoT based system for lake water quality monitoring,” PLoS One, vol. 19, no. 3 March, Mar. 2024, doi: 10.1371/journal.pone.0299089.

I. Essamlali, H. Nhaila, and M. El Khaili, “Advances in machine learning and IoT for water quality monitoring: A comprehensive review,” Mar. 30, 2024, Elsevier Ltd. doi: 10.1016/j.heliyon.2024.e27920.

D. J. Kovacs et al., “Membrane fouling prediction and uncertainty analysis using machine learning: A wastewater treatment plant case study,” J. Memb. Sci., vol. 660, Oct. 2022, doi: 10.1016/j.memsci.2022.120817.

M. Ahmed, R. Mumtaz, Z. Anwar, A. Shaukat, O. Arif, and F. Shafait, “A Multi–Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote Sensing,” Water (Switzerland), vol. 14, no. 13, Jul. 2022, doi: 10.3390/w14132112.

D. N. Khoi, N. T. Quan, D. Q. Linh, P. T. T. Nhi, and N. T. D. Thuy, “Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam,” Water (Switzerland), vol. 14, no. 10, May 2022, doi: 10.3390/w14101552.

P. J. García-Nieto, E. García-Gonzalo, J. R. A. Fernández, C. D. Muñiz, and L. A. Menéndez-García, “Machine Learning-Driven Prediction of Quality Water in the Ebro River from the Ascó Water Quality Monitoring Station Data, Northeast Spain,” Applied Sciences, vol. 16, no. 13, p. 6475, Jun. 2026, doi: 10.3390/app16136475.

F. Huang and Y. Deng, “TCGAN: Convolutional Generative Adversarial Network for time series classification and clustering,” Neural Networks, vol. 165, pp. 868–883, Aug. 2023, doi: 10.1016/j.neunet.2023.06.033.

J. Kim and J. Kim, “Classification of Breathing Signals According to Human Motions by Combining 1D Convolutional Neural Network and Embroidered Textile Sensor,” Sensors, vol. 23, no. 12, Jun. 2023, doi: 10.3390/s23125736.

I. T. Nicholaus, J. S. Lee, and D. K. Kang, “One-Class Convolutional Neural Networks for Water-Level Anomaly Detection,” Sensors, vol. 22, no. 22, Nov. 2022, doi: 10.3390/s22228764.

T. Schlagenhauf, J. Wolf, and A. Puchta, “Convolutional-Based Encoder–Decoder Network for Time Series Anomaly Detection during the Milling of 16MnCr5,” Data (Basel)., vol. 7, no. 12, Dec. 2022, doi: 10.3390/data7120175.

“BERITA NEGARA REPUBLIK INDONESIA.” [Online]. Available: www.peraturan.go.id

M. Has, D. Kreković, M. Kušek, and I. Podnar Žarko, “Efficient Data Management in Agricultural IoT: Compression, Security, and MQTT Protocol Analysis,” Sensors, vol. 24, no. 11, Jun. 2024, doi: 10.3390/s24113517.

S. Bouziane, B. Aghoutane, A. Moumen, A. El Ouali, A. Essahlaoui, and A. El Hmaidi, “An intelligent and explainable IoT-Edge-Cloud architecture for real-time water quality monitoring,” IAES International Journal of Artificial Intelligence, vol. 15, no. 2, pp. 1109–1120, Apr. 2026, doi: 10.11591/ijai.v15.i2.pp1109-1120.

P. Li, J. Zhang, and P. Krebs, “Prediction of Flow Based on a CNN-LSTM Combined Deep Learning Approach,” Water (Switzerland), vol. 14, no. 6, Mar. 2022, doi: 10.3390/w14060993.

N. G. Rezk, S. Alshathri, A. Sayed, and E. El-Din Hemdan, “EWAIS: An Ensemble Learning and Explainable AI Approach for Water Quality Classification Toward IoT-Enabled Systems,” Processes, vol. 12, no. 12, Dec. 2024, doi: 10.3390/pr12122771.

U. Panahi and C. Bayılmış, “Enabling secure data transmission for wireless sensor networks based IoT applications,” Ain Shams Engineering Journal, vol. 14, no. 2, Mar. 2023, doi: 10.1016/j.asej.2022.101866.

D. S. Rajput, G. Meena, M. Acharya, and K. K. Mohbey, “Fault prediction using fuzzy convolution neural network on IoT environment with heterogeneous sensing data fusion,” Measurement: Sensors, vol. 26, Apr. 2023, doi: 10.1016/j.measen.2023.100701.

M. J. C. S. Reis, “Lightweight Signal Processing and Edge AI for Real-Time Anomaly Detection in IoT Sensor Networks,” Sensors, vol. 25, no. 21, Nov. 2025, doi: 10.3390/s25216629.

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