KNN-MVO-SMOTE Algorithm for Air Quality Imbalanced Data Classification
DOI:
https://doi.org/10.59395/ijadis.v6i3.1424Keywords:
Accuracy, Data Imbalance, Environmental Analysis, Machine Learning, OptimizationAbstract
This research addresses air pollution, a pressing global issue influenced by geographic and temporal factors, using advanced machine-learning techniques to enhance air quality classification. By integrating the K-Nearest Neighbors (KNN) algorithm with the Synthetic Minority Over-sampling Technique (SMOTE) and Multi-Verse Optimization (MVO), we tackle challenges like data imbalance and parameter optimization. Our novel approach, which combines SMOTE and MVO within the KNN framework, has significantly increased classification accuracy to 97%, substantially improving over previous methods. The dataset includes diverse geographic and temporal data, with potential biases acknowledged and addressed. This study highlights the efficacy of merging MVO and SMOTE to optimize classification models, making a substantial contribution to environmental analysis and the fight against air pollution. Future research will explore AutoML technology to improve algorithmic optimization, offering more efficient and adaptive solutions. This pioneering effort emphasizes the critical role of technological innovation in tackling environmental challenges and marks a significant advancement in combating global air pollution.
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Copyright (c) 2025 Muhammad Miftahur Rizky, Muhammad Itqan Mazdadi, Muliadi Muliadi, Mohammad Reza Faisal, Fatma Indriani, Hasri Akbar Awal Rozaq, Oktay Yildiz
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