Prediction of Turbidity Removal Time in Electrocoagulation Wastewater Using Random Forest, XGBoost, and Others: A Data-Driven Information System Approach

Sinung Suakanto; Tan Lian See; Zatul Alwani Shaffiei; Taufiq Maulana  Firdaus; Muharman  Lubis; Anggera  Bayuwindra

doi:10.52436/1.jutif.2025.6.4.4847

Authors

Sinung Suakanto Information System, Telkom University, Indonesia
Tan Lian See Malaysia - Japan International Institute of Technology, Universiti Teknologi Malaysia, Malaysia
Zatul Alwani Shaffiei Malaysia - Japan International Institute of Technology, Universiti Teknologi Malaysia, Malaysia
Taufiq Maulana Firdaus Information System, Telkom University, Indonesia
Muharman Lubis Information System, Telkom University, Indonesia
Anggera Bayuwindra School of Electrical Engineering and Informatics, Bandung of Institute of Technology, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.4.4847

Keywords:

Electrocoagulation Turbidity Removal, Random Forest, Wastewater Treatment, Water Resource Asset

Abstract

Electrocoagulation is an effective and environmentally friendly technology for treating wastewater by removing contaminants such as turbidity, heavy metals, and organic compounds. Accurately predicting turbidity removal time is essential for optimizing treatment performance and operational efficiency. However, this is challenging due to complex, nonlinear relationships between multiple parameters including current, voltage, electrode configuration, conductivity, and turbidity removal rate. This study aims to develop a predictive framework by comparing six supervised regression models, namely Linear Regression, Polynomial Regression, Random Forest, Support Vector Regression (SVR), XGBoost, and Long Short-Term Memory (LSTM), using key electrocoagulation parameters. After extensive data preprocessing, a dataset of 281 samples was used for training and validation. Among them, Random Forest achieved the best performance (R² = 0.876, RMSE = 601.15). A data-driven information system is proposed to integrate these predictive capabilities for real-time monitoring and control. By improving turbidity prediction accuracy, the system enables the sustainable utilization of water as a valuable asset, even in its wastewater form. The approach enhances decision-making by providing intelligent feedback for process optimization. This research contributes to the advancement of intelligent, sustainable wastewater treatment systems by integrating machine learning prediction models with practical process control applications in informatics.

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