PREDIKSI PERTUMBUHAN BERAT IKAN LELE DALAM BUDIDAYA AKUAKULTUR DENGAN MENGGUNAKAN ALGORITMA RANDOM FOREST REGRESSION

DAHLAN, DIMAS HUMAYUN DANU and Utami, Alvi Syahrini and Rizqie, Muhammad Qurhanul (2024) PREDIKSI PERTUMBUHAN BERAT IKAN LELE DALAM BUDIDAYA AKUAKULTUR DENGAN MENGGUNAKAN ALGORITMA RANDOM FOREST REGRESSION. Undergraduate thesis, Sriwijaya University.

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Abstract

The weight growth of fish is a fundamental aspect of aquaculture, serving as a pivotal indicator of feeding efficacy and pond environmental management. This study aims to develop a predictive model for the growth of catfish weight using the Random Forest Regression algorithm approach. The research method involves the utilization of a dataset comprising environmental parameters such as temperature, dissolved oxygen, pH, and nitrate levels, as well as the length and weight data of catfish from the previous day to train the Random Forest Regression model. The results indicate that the developed model can provide predictions of catfish weight growth with a high level of accuracy. By considering environmental parameters and previous length and weight data of the fish, this model can offer reliable estimates regarding catfish weight growth. The implications of this research are that the use of this model can assist catfish farmers in optimizing environmental management to effectively enhance catfish growth and productivity. Thus, this study highlights the potential of utilizing the Random Forest Regression algorithm as a useful method in predicting catfish weight growth and reinforcing sustainable catfish farming practices.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Akuakultur, Lele Dumbo, Prediksi Berat, Random Forest Regression
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Dimas Humayun Danu Dahlan
Date Deposited:	26 Jun 2024 06:11
Last Modified:	26 Jun 2024 06:11
URI:	http://repository.unsri.ac.id/id/eprint/147939

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