Analisis Pola Konvergensi Transpor Kelembapan Udara di Indonesia Bagian Barat Menggunakan K-Means dengan Pembobotan Statistik dan Hierarchical Shape-Based Clustering - Pratiwi, Asri; Azis, Tukhfatur Rizmah; Fitrianto, Anwar; Erfiani, Erfiani; Jumansyah, L.M. Risman Dwi (KUBIK: Jurnal Publikasi Ilmiah Matematika, 2024)
This study analyzes the convergence patterns of Vertically Integrated Moisture Transport (VIMT) in the western region of Indonesia using the K-Means method with statistical weighting and Hierarchical Shape-Based Clustering based on Dynamic Time Warping (DTW). Daily data on specific humidity, zonal wind speed, and meridional wind speed from 2020–2023 were used to calculate VIMT. Clustering methods were utilized to identify grouping patterns in moisture transport data. The results showed that moisture convergence significantly increased during the rainy season (November–February). Using the K-Means method, five clusters with clearer separations were obtained compared to the four clusters produced by the Hierarchical Clustering method. Performance evaluation using Silhouette and Calinski-Harabasz scores indicated that the K-Means method was superior, with scores of 0.37 and 104.88 compared to 0.13 and 96.34 for the Hierarchical method. This provides an understanding of the moisture transport patterns, serving as a reference for predicting weather and climate patterns, thereby supporting efforts to mitigate the impacts of extreme weather in Western Indonesia.