<![CDATA[Dynamic and irregular daily weather changes present major challenges in understanding seasonal patterns. Data uncertainty, outliers, and inter-season variability further complicate weather analysis using conventional methods. To address this issue, this study integrates Density-Based Spatial Clustering of Application with Noise (DBSCAN) and Gaussian Mixture Model (GMM) to analyze daily weather patterns in Makassar City. A total of 2,192 daily records from 2019 to 2024, including rainfall, specific humidity, atmospheric pressure, and wind speed, were examined. DBSCAN detected one dominant cluster (2019 data) and 173 outliers. The main cluster was further partitioned by GMM into three sub-clusters representing the wet (511 records, 13.39 mm rainfall), dry (633 records, 0.15 mm), and transition (875 records, 2.53 mm) seasons. GMM identified 1,764 fixed clusters and 255 ambiguous data points, with a log-likelihood of 5091.22 and the highest Silhouette Score of 0.188. Comparative evaluation demonstrated that the hybrid DBSCAN-GMM achieved superior performance (Silhouette Score = 0.1434) compared to DBSCAN or GMM individually. The novelty of this research lies in applying the DBSCAN-GMM integration, which is rarely used in tropical weather analysis, to capture seasonal structure and anomalies adaptively. This study contributes methodologically to clustering-based weather modeling and practically supports applications such as agricultural planning, disaster mitigation, and adaptive climate strategies in tropical regions.]]>
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