Evaluasi Performa Otomatisasi Skema Basis Data Dengan Model dan Migrasi Django Dalam Aplikasi Proyek Akhir

Abstract

Effective management of database schemas is essential to ensure the scalability, performance, and integrity of academic applications. However, for systems with complex entities and large volumes of data, the use of framework-based schema automation remainsrelatively untested. Examiner, Guidance, Evaluation, Final Project, Lecturer, and Student are the six main entities that make up the final project management application, and the purpose of this study is to assess how well Django's models and migration tools can automate database schemas for this application. The case study methodology was used on two linked datasets with 1,000 and 10,000 entries, respectively. During the stages of installation, testing, and analysis, a descriptive-analytical approach was employed. Unit, functional, integration, and performance tests were conducted using MariaDB, Django 5.1.2, and a digital stopwatch. For the dataset with 1,000 entries, the read operation averaged 0.00010 seconds, the update operation 0.00439 seconds, and the delete operation 0.00124 seconds. The results demonstrate that the models remain consistent, migrations proceed smoothly, and all CRUD operations are completed with an effective average time. For the dataset containing 10,000 entries, the average operation times were 0.00045 seconds per operation, 0.00013 seconds for reads, 0.04535 seconds for updates, and 0.00345 seconds for deletions. In summary, Django can be effectively applied to large-scale academic applications, as it consistently and efficiently automates complex database schemas.