System Integration for Judicial Prediction of Case Type and Resolution Duration at Bekasi Religious Court
DOI:
https://doi.org/10.38035/systemic.v2i1.69Keywords:
Judicial System, Hybrid Machine Learning, Case Management, Digital Transformation, Predictive AnalyticsAbstract
The administration of justice in Religious Courts faces inefficiencies due to manual case classification and unpredictable resolution timelines. This research addresses the gap in systemic judicial management tools by proposing a hybrid machine learning architecture. The objective is to design a systemic information model integrating Deep Learning for case type classification and Ensemble Learning for duration prediction. The method employs CRISP-DM framework combining Long Short-Term Memory (LSTM) for text analysis and XGBoost for temporal regression on 4,461 case records from Bekasi Religious Court. Results indicate a classification accuracy of 93.9% and a Mean Absolute Error (MAE) of 12.4 days for duration prediction. The novelty lies in the systemic integration of dual predictive models into judicial workflows, proposing a digital transformation model for court administration. Conclusions affirm that hybrid AI systems significantly enhance resource planning and administrative efficiency
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