Implementasi Isolation Forest dalam Pemantauan Suhu dan pH Air untuk Optimalisasi Pertumbuhan Ikan pada Kolam Budi Daya Berbasis Web
DOI:
https://doi.org/10.47065/bulletincsr.v6i6.1270Keywords:
Isolation Forest; Water Quality; Anomaly Detection; Weather Variation; Early Warning System; Fish FarmingAbstract
This study aims to implement the Isolation Forest algorithm in a water temperature and pH monitoring system to enhance the effectiveness of water quality supervision and support the optimization of fish growth in aquaculture ponds at Cisauk, which manages 18 fish cages with a total population of approximately 40,000 fish of various species, namely tilapia, Pangasius (patin), gourami, and catfish (lele). One of the primary challenges faced in this environment is the fluctuation of water temperature and pH caused by environmental changes, particularly weather variables such as heavy rainfall that can drastically lower pH, high solar intensity that significantly raises water temperature, and seasonal transitions that disrupt the stability of water quality parameters. These changes often occur suddenly, potentially inducing physiological stress, reduced metabolism, impaired growth, and even increased fish mortality. The monitoring system previously used was not yet computerized, so data recording relied on manual measurement, which could not provide real-time information or detect critical conditions quickly and accurately. In this study, temperature and pH data were obtained through digital sensors integrated with a Web-based monitoring system serving as a medium for data visualization and management. The Isolation Forest algorithm was applied to automatically detect anomalies in water quality parameters by leveraging deviation patterns in the data caused by weather changes and other environmental factors. The system was developed using the Prototyping model so that the system design could be adjusted iteratively based on field user needs. The implementation results show that the Isolation Forest algorithm was successfully applied to the system. The dataset used had a temperature range of 26.10°C–33.20°C and a pH range of 6.69–9.62. Testing on 450 historical data points showed that the model successfully identified 427 normal data points and 23 anomalous data points, with an accuracy rate of 94.89%, where anomalous data were generally marked by higher temperature and pH than normal data, with an average anomalous temperature of 31.26°C and an average anomalous pH of 8.79 due to the influence of hot weather. The integration of this algorithm with the Web-based Dashboard also proved effective in providing early warnings to staff, enabling corrective actions to be taken in a timely manner. Overall, this system has proven capable of detecting water quality anomalies accurately and quickly, thereby having the potential to improve operational efficiency, maintain water quality stability, and support the sustainability of fish farming at Cisauk.
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