Implementation of the K-Means Method for Developing an Air Quality Monitoring Information

Abstract

Air quality in Bogor City is becoming increasingly complex due to the rising number of motor vehicles, small-scale industrial activities, and seasonal dynamics that are difficult to analyze using conventional methods. The Environmental Agency of Bogor City routinely monitors air quality through the Air Quality Monitoring System (AQMS); however, data utilization remains confined to monitoring and reporting, necessitating advanced analysis to achieve a more comprehensive view of air quality patterns. This study aims to classify time periods based on air quality parameters using the K-Means clustering method to identify good and bad air pollution categories. The research data was obtained from the Bogor City Environmental Agency's AQMS for the period from January 2023 to June 2025. The results indicate that the K-Means method successfully clustered the data into two groups: good and bad air quality categories. Good air quality was identified in 2023 (January, February, March, April, November, and December), 2024 (January, February, March, April, October, November, and December), and 2025 (January to May). Conversely, poor air quality occurred in 2023 (May to October), 2024 (May to September), and 2025 (June). The findings of this research are expected to support pollution control strategies and early warning systems based on air quality data.