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Application of Machine Learning Models in Water Quality Classification in Lake Maninjau: Random Forest as the Optimal Solution

Abdurrahman Niarman(1), Reni Kurnia(2), Iswandi Iswandi(3),
(1)   Indonesia
(2) Universitas Negeri Padang  Indonesia
(3) Universitas Islam Negeri Mahmud Yunus Batusangkar  Indonesia

Corresponding Author


DOI : https://doi.org/10.24036/et.v12i1.128941

Full Text:    Language : id

Abstract


This research develops a machine learning model to classify water quality in Lake Maninjau using data from the Ministry of Environment and Forestry's Onlimo application. The dataset includes parameters such as temperature, pH, DO, conductivity, TDS, salinity, turbidity, nitrate and ammonium. Four machine learning algorithms were tested: Logistic Regression, SVM, Gradient Boosting, and Random Forest. As a result, Random Forest shows the best performance with an average accuracy of 87.33% and a standard deviation of 6.97%, and a test accuracy of 90.63%. This model is effective in monitoring and managing water quality, supporting authorities in water resource management decision making. This research also shows how the integration of machine learning and IoT can provide practical solutions in environmental monitoring.


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