<![CDATA[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.]]>
