Machine Learning Approaches for Fish Pond Water Quality Classification: Random Forest, Gaussian Naive Bayes, and Decision Tree Comparison

Danuri, Danuri; Pozi, Muhammad Mohd

Machine Learning Approaches for Fish Pond Water Quality Classification: Random Forest, Gaussian Naive Bayes, and Decision Tree Comparison

Danuri, Danuri and Pozi, Muhammad Mohd (2024) Machine Learning Approaches for Fish Pond Water Quality Classification: Random Forest, Gaussian Naive Bayes, and Decision Tree Comparison. In: Proceedings of the 11th International Applied Business and Engineering Conference, ABEC 2023, September 21st, 2023, Bengkalis, Riau, Indonesia.

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Abstract

The health and production of fish in fish farms are greatly influenced by the water quality. This study examines three Machine Learning(ML) methods for categorizing fish pond water quality: Random Forest(RF), Gaussian Naive Bayes(GNB), and Decision Tree(DT). Accuracy, precision, recall, and the F1-S

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Date Deposited:	04 Mar 2026 17:14
Last Modified:	17 Apr 2026 00:23
URI:	http://eprints.eai.eu/id/eprint/47446

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