IoT-Based Water Quality Monitoring and Sustainable Modeling  for Smart Campuses

Fachrul Kurniawan; Miladina Rizka  Aziza; Novrindah Alvi  Hasanah; Fadia Irsania Putri; Aji Prasetya Wibawa; Jehad Hammad; Yuhefizar

doi:10.29207/resti.v10i2.7360

Authors

Fachrul Kurniawan Universitas Islam Negeri Maulana Malik Ibrahim
Miladina Rizka Aziza Universitas Islam Negeri Maulana Malik Ibrahim
Novrindah Alvi Hasanah Universitas Islam Negeri Maulana Malik Ibrahim
Fadia Irsania Putri Universitas Negeri Malang
Aji Prasetya Wibawa Universitas Negeri Malang
Jehad Hammad Al-Qud Open University Palestine
Yuhefizar Politeknik Negeri Padang

DOI:

https://doi.org/10.29207/resti.v10i2.7360

Keywords:

IoT-based monitoring, monitoring water, smart green campus, visualization

Abstract

This study proposes an IoT-based water quality monitoring framework integrated with a continuous sustainable modeling approach for smart campus applications. A total of 404 sensor observations were collected, including pH, turbidity, temperature, and Total Dissolved Solids (TDS). A continuous water suitability score ranging from 0 to 1 was constructed based on WHO drinking water standards, and Multiple Linear Regression was employed to model the relationship between water quality parameters and the suitability score. The main contribution of this study lies in the development of a lightweight analytical framework that combines continuous regression modeling with threshold-based classification to support real-time decision-making in resource-constrained environments. The dataset was divided into 90% training and 10% testing data. The results show that the proposed framework achieved a classification accuracy of 88.5% based on threshold mapping of regression outputs, with a misclassification rate of 11.5%. These findings demonstrate the effectiveness of integrating IoT-based monitoring with interpretable and computationally efficient analytical models for sustainable campus water management.

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