<![CDATA[This research develops a web-based recommendation system for computer assembly simulations using genetic algorithms. The system is designed to assist users in selecting optimal computer components based on their available budget and desired performance. Component data were collected from e-commerce platforms and online sources, then preprocessed using Min-Max normalization to ensure balanced data scaling. The system was developed using Laravel for the frontend interface and Flask API for computational processing of the genetic algorithm. System evaluation was conducted using the System Usability Scale (SUS) method involving 21 respondents, resulting in an average score of 86.67, which falls into the "Excellent" category and Grade B on the usability scale. Additionally, performance comparisons with prebuilt systems from online stores show that the recommendation system produced assemblies with lower costs and higher performance. The implementation of selection, crossover, and mutation in the genetic algorithm effectively evaluates component combinations to achieve optimal configurations. This research contributes to the development of intelligent optimization-based systems that simplify the computer assembly process, particularly for novice users with limited technical knowledge and constrained budgets.]]>
Copyrights © 2025
