Web-Based Smart Aquaculture: Comparative Analysis of Mamdani, Sugeno, and Tsukamoto Fuzzy Inference Systems for Shrimp Pond Water Quality Assessment

Abstract

Indonesia possesses vast marine and aquaculture potential; however, national shrimp production in 2024 achieved only 56.67% of its target, largely due to suboptimal water quality management. To address this issue, an intelligent classification system capable of handling uncertainty in aquaculture environments is required. This study presents a comparative evaluation of three fuzzy inference systems (FIS), namely Mamdani, Sugeno, and Tsukamoto, for shrimp pond water quality classification based on four key parameters: temperature, pH, salinity, and dissolved oxygen (DO). Water quality conditions were categorized into four classes: Good, Medium, Bad, and Very Bad using trapezoidal membership functions and expert-defined reference labels derived from aquaculture water quality standards. The dataset consisted of 994 water quality records collected from shrimp ponds in Surabaya, Indonesia, during the period from December 2024 to April 2025. Experimental results indicate that the Mamdani method produced the highest consistency with the expert-defined reference rules, achieving an agreement accuracy of 0.800, precision of 0.825, recall of 0.800, and F1-score of 0.797. In comparison, both Sugeno and Tsukamoto produced lower performance with an accuracy of 0.700 and F1-score of 0.728, although they achieved slightly higher precision values of 0.880. The findings indicate that the Mamdani fuzzy inference system provides more stable and consistent inference behavior relative to the predefined aquaculture reference rules for shrimp pond water quality assessment. Furthermore, the proposed web-based monitoring system demonstrates the practical potential of fuzzy logic approaches in supporting sustainable smart aquaculture management and environmental monitoring.