Meningkatkan Performa dalam Pengelolaan Data Ketidaktidakan dengan Menggunakan B-Tree Indexing

Abstract

Irregularity data management in the logistics industry plays an important role in ensuring smooth operations and maintaining service quality. However, the Excel-based manual system still used by many companies often faces various obstacles, such as unstructured data, redundancy, and slow and inefficient access. This study aims to improve the efficiency of irregularity data management by developing a web-based management application that integrates data normalization and indexing using the B-Tree structure. The novelty of this study lies in the application of a combination of data normalization methods and B-Tree indexing structures in the context of irregularity data management in the logistics industry, which has not been widely applied in an integrated manner in previous studies. The normalization process is designed to organize data, reduce redundancy, and improve data integrity. Meanwhile, B-Tree indexing is applied to accelerate the process of searching and processing data, allowing for faster and more accurate access. Testing was conducted using historical data from logistics companies to evaluate the performance of the developed system. The results showed a significant increase in the speed of reporting, searching, and data analysis compared to the manual system. This application also provides real-time access, which supports more efficient and data-driven strategic decision making. Thus, this study provides an effective technology-based solution to address the challenges of irregular data management, as well as contributing to improving operational efficiency and service quality in the logistics industry.