Analisis K-Medoid Untuk Pemetaan Tingkat Pencemaran Udara di Provinsi Sulawesi Selatan - Irwan; Wahyuni, Maya Sari; Sulaiman (Journal of Mathematics, Computations and Statistics, 2022)
Cluster analysis serves to group objects with high similarity of characteristics in one cluster while objects with dissimilarity of characteristics are in different clusters. Cluster analysis is divided into two, namely hierarchical and non-hierarchical. This study applies a non-hierarchical cluster analysis, namely the k-medoid method to group districts/cities and their four sectors, namely transportation, industrial/agroindustrial, residential, office/commercial in South Sulawesi Province based on indicators that make up the 2019 Air Quality Index (AQI) value and 2020. AQI are categorized based on six Environmental Quality Index (EQI) statuses. To get the best clusters from the k-medoid process, each cluster needs to be evaluated using the silhouette coefficient value. The results of this study indicate that k = 2 clusters from the k-medoid method are the best cluster initiations with the best silhouette coefficient value of 0.56. The results of the analysis of the cluster results show that with the use of 2 clusters, for 2019 passive sampler data, cluster 1 is included in the very good EQI category with a AQI value of 84.14 and cluster 2 is in the less EQI category with an AQI value of 60.04. For the 2020 passive sampler data, cluster 1 is included in the good EQI category with a AQI value of 80.68 and cluster 2 is in the less EQI category with a AQI value of 61.53.