IMPLEMENTATION OF RULE BASED AND STRING MATCHING IN THE AGGREGATE POPULATION DATA MIGRATION ETL PROCESS
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The migration of aggregated population data requires a structured and reliable process to ensure data accuracy, consistency, and integrity. This study aims to implement a rule-based approach and string matching techniques within the Extract, Transform, Load (ETL) process for aggregated population data migration. The rule-based method is applied during the transformation phase to standardize data formats, validate attributes, and enforce predefined business rules. Meanwhile, string matching techniques are utilized to identify similarities between textual data, resolve inconsistencies, and support data cleansing, particularly in handling variations in names, regions, and administrative records.
The research methodology involves requirements analysis, system design, implementation, and testing. The ETL system is developed to extract data from multiple sources, transform it based on defined rules and matching algorithms, and load it into a centralized database. Performance evaluation is conducted by measuring accuracy, processing time, and data consistency after migration.
The results show that the integration of rule-based processing and string matching improves the quality of migrated data by reducing duplication, minimizing errors, and enhancing data uniformity. Furthermore, the system demonstrates efficiency in handling large-scale population datasets. This study contributes to the development of more reliable data migration systems, particularly in the context of population data management, and supports better decision-making processes in government institutions.
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