Application of K-Means Clustering Algorithm for Air Quality Pattern Analysis in Jakarta - Razan, Muhammad Arya Fayyadh; Alifah, Nur Juzieatul; A’yuni, Qurrata; Wati, Masna; -, Haviluddin (Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia, 2025)
Air pollution in urban areas, particularly in Jakarta, is a significant issue that impacts public health and environmental quality. This study aims to analyze air quality patterns in Jakarta from 2010 to 2023 using the K-Means Clustering method based on Air Pollution Standard Index (ISPU) data. Data processing stages based on the CRISP-DM methodology are applied to process and analyze data systematically. The stages include business understanding, data understanding, data preparation, modeling, and evaluation. The results showed that the data were divided into three distinct clusters: healthy, unhealthy, and moderate. Cluster 0, which includes stations DKI1 and DKI2, shows better air quality, while cluster 2, which consists of stations DKI3, DKI4, and DKI5, shows higher pollution levels. These findings offer valuable insights for policymakers in developing more effective air pollution control strategies. Thus, the results of this study not only contribute to the understanding of air quality in Jakarta but also emphasize the need for data-driven mitigation actions to improve public health and the environment.