Digital Record Classification Using SVM on Permissioned Blockchain Hyperledger Fabric for Regional Status Visualization

Abstract

This paper proposes a simulation-based model for classifying public aspiration records using the Support Vector Machine (SVM) Linear algorithm integrated with a permissioned blockchain network, Hyperledger Fabric. A total of 1,000 simulated text entries were manually labeled into two categories, complaints and aspirations, and three urgency levels (high, medium, and low) by the researchers. Text preprocessing included case folding, stopword removal, stemming, and TF–IDF vectorization. The model was evaluated using 5-fold cross-validation with an 80:20 train-test split and random seed 42, producing an accuracy of 77.5%, an F1-score of 0.78, and AUC of 0.86 for category classification, and 35.5% accuracy with AUC 0.58 for urgency classification. Integration testing with Hyperledger Caliper achieved 128 transactions per second throughput, 182 ms latency, and 2.4 s block commit time with an average block size of 412 KB, demonstrating efficient and verifiable data management. Although based on simulated data, the proposed SVM Blockchain architecture provides an initial foundation for secure, transparent, and data-driven decision-making in digital government systems.