Analysis of Plantation Asset Clustering Based on Hierarchical Clustering in Cybersecurity Systems Using Big Data Analytic Security

Data security of plantation assets is a major challenge in the digital era, especially with the increasing cyber threats that can disrupt the operations and sustainability of this sector. This research develops an innovative framework for asset grouping based on hierarchical clustering integrated with blockchain technology. This framework is designed to cluster assets based on risk profiles, securely store clustering results, and ensure transparency through blockchain. The research uses a big data analytics approach to handle the complexity of multidimensional data originating from IoT, GIS, and financial data. The research results show that the developed framework is capable of producing asset clustering with an average Silhouette Score of 0.7, demonstrating high clustering effectiveness. The blockchain system ensures the security and auditability of clustering results, providing transparency in asset data management. However, challenges such as clustering parameters and real-time implementation still need to be explored further. This framework has great potential to be applied in various other domains that require risk-based data management