Penerapan K-Means Clustering pada Dataset Global Disaster Response Menggunakan RapidMiner - Padilah, Muhamad Rizqi; Firmansyah, Hasbi; Asriyani, Wahyu; Fitria, Ria Indah (Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research, 2025)
This study applies the K-Means clustering method to the Global Disaster Response dataset from 2018 to 2024 to identify disaster patterns based on impact and response characteristics. The research employs a quantitative descriptive approach using data mining techniques and follows the Knowledge Discovery in Database (KDD) framework, including data understanding, preprocessing, clustering, evaluation, and interpretation. Data processing and clustering were performed using RapidMiner. The K-Means algorithm was tested with cluster numbers ranging from K = 2 to K = 10, and cluster quality was evaluated using the average within-centroid distance (avg). The results show a consistent decrease in avg values, from 6.336 (K2), 5.303 (K3), 4.824 (K4), 4.446 (K5), 4.245 (K6), 3.999 (K7), 3.844 (K8), 3.681 (K9), to 3.558 (K10). Based on the Elbow Method analysis, the most significant reduction occurs between K2 and K3, indicating that K = 3 is the optimal number of clusters. The resulting clusters represent low, medium, and high disaster risk levels. These findings demonstrate that the integration of K-Means and the Elbow Method using RapidMiner is effective for segmenting global disaster data and supporting disaster risk analysis and mitigation planning.