Model Hybrid Rule-Based dan KNN untuk Kecocokan Ikan Hias Terhadap Kualitas Air
DOI:
https://doi.org/10.30998/jrami.v7i04.2034Keywords:
Ikan Hias, Kualitas Air, K-Nearest Neighbor, Forward Chaining, Sistem Pendukung KeputusanAbstract
The ornamental fish farming industry requires consistent water quality management, yet PT Ikan Bangun Indonesia still relies on manual expert judgment to determine fish-water compatibility, making the process subjective and prone to delayed detection of critical conditions. This research develops a web-based decision support system that combines a Rule-Based System using Forward Chaining as a lethal-condition filter (Layer I) with a K-Nearest Neighbor algorithm based on Euclidean distance as a suitability ranking layer (Layer II), covering ten ornamental fish species and seven water quality parameters. The system was built using a three-tier architecture with React.js, Laravel, PostgreSQL, and a Python FastAPI microservice, and was evaluated through Black Box Testing, White Box Testing using basis path testing, and a Confusion Matrix approach. The results show that all functional scenarios passed testing, all internal logical paths executed correctly, and the hybrid model classified water feasibility with 100% accuracy, precision, and recall. This study concludes that the hybrid Rule-Based and KNN approach is effective and reliable for automated decision-making in ornamental fish aquaculture
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Copyright (c) 2026 Muhamad Iqbal Nurdin Siregar, Aniq Astofa (Author)
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