Application of Algorithm Learning Vector Quantization for Air Quality Classification - Roufsaldiaz Nawfal; Dina Fitria; Chairina Wirdiastuti (Universitas Negeri Padang, 2025)
This study aims to classify air quality using the Learning Vector Quantization (LVQ) algorithm based on the Air Quality and Pollution Assessment dataset obtained from Kaggle. The dataset comprises 5,000 observations, of which 4,000 were used for training and 1,000 for testing. The analytical process includes data preprocessing (normalization), the construction and training of the LVQ model, and performance evaluation using a confusion matrix. The experimental results demonstrate that the LVQ model successfully classified 903 of 1,000 test samples, yielding an overall accuracy of 90.3%. This level of accuracy indicates that the LVQ algorithm can capture relevant patterns in air quality variables and perform reliable classification across different air quality categories. The findings suggest that LVQ can serve as a potential foundation for developing automated air quality monitoring and decision-support systems. Future studies are encouraged to compare LVQ with other machine learning classification techniques to build a more optimal model and to gain deeper analytical insights.