Bibliometrik Analysis: Optimasi Regresi Linear untuk Estimasi Big Data pada Database Scopus Tahun 2013-2024
Keywords:
Linear Regression, Big Data, Optimization,, Quantum Algorithms, Data AnalyticsAbstract
This bibliometric analysis investigates the optimization of linear regression for big data estimation, focusing on publication trends, citation metrics, geographic distribution, and research innovations from 2013 to 2024. The publication trend analysis reveals a significant increase in research on linear regression optimization, peaking in 2023, followed by a decline in 2024. Citation analysis shows that although this topic is relatively new, it has gained increasing scientific recognition, indicating its growing relevance. The geographic distribution highlights China, the United States, and the United Kingdom as the leading contributors to research on linear regression optimization for big data. Key innovations in this field include the application of quantum algorithms and advanced optimization techniques, which have significantly improved computational efficiency and the accuracy of linear regression models in handling large and complex datasets. These findings underscore that linear regression optimization will continue to evolve and make important contributions to big data analytics.
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