<![CDATA[This study develops an integrated fuzzy-logic-based assessment system to evaluate well water quality using pH, salinity (via TDS conversion), and turbidity. The research aims to develop an adaptive system capable of handling uncertainties in water quality parameters in real-time monitoring across Semarang's coastal areas. The methodology incorporates sensor performance validation, fuzzy input processing, rule-based inference, and defuzzification implemented through MATLAB R2021a. Experimental results demonstrate exceptional metrological performance, with sensor linearity achieving determination coefficients (R²) of 0.9995 for pH, 0.9998 for salinity, 0.9996 for temperature, and 0.9993 for turbidity. Statistical validation confirmed measurement precision, with root-mean-square errors (RMSEs) of 0.018 pH units, 0.023 ppt salinity, 0.14°C temperature, and 0.027 NTU turbidity. Field implementation across 6 sub-districts revealed that 83.3% of samples complied with pH quality standards, 60% met turbidity thresholds, while 33.3% of samples in Genuksari exhibited seawater intrusion indicators with salinity levels exceeding 0.5 ppt. The study conclusively demonstrates that the developed fuzzy logic system provides accurate, consistent water quality evaluation and presents a viable framework for smart water monitoring infrastructure in coastal urban environments, particularly for detecting saline intrusion and maintaining water security.]]>
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