PERBANDINGAN ALGORITMA CLUSTERING K-MEANS, GAUSSIAN MIXTURE MODEL, DAN DBSCAN PADA DATA INDEKS STANDAR PENCEMAR UDARA (ISPU) DI PROVINSI DKI JAKARTA
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Abstract
The purpose of this study is to compare the performance of three clustering algorithms, namely K-Means Clustering, Gaussian Mixture Model (GMM), and DBSCAN, when analyzing the Air Pollution Standard Index (ISPU) data of DKI Jakarta Province. Air quality parameters such as nitrogen dioxide, ozone, sulfur dioxide, carbon monoxide, and PM10 were used. The research process includes a preprocessing stage, where missing values are used with the average imputation method and the data is normalized with Standard Scaler. With a Silhouette Score of 0.2784, Calinski-Harabasz Score of 403.111, and Davies-Bouldin Index of 1.2481, K-Means Clustering showed the best results of the three-clustering metrics. Meanwhile, of the threes coring metrics, GMM and DBSCAN showed inferior results. The results show that the K-Means Clustering algorithm performs better clustering of Jakarta ISPU data than other algorithms, with advantages in compactness and better cluster separation. With the use of additional clustering algorithms and more in-depth data analysis, this research opens up opportunities for further development.
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