A Decision Support System for Determining Optimal Concrete Quality Using the Simple Additive Weighting (SAW) Algorithm (Case Study: UISU Concrete Laboratory)

Abstract

This study aims to design a decision support system to determine the best concrete quality using the Simple Additive Weighting (SAW) algorithm. Concrete is the primary material in construction, possessing various mechanical properties and characteristics that define its quality. At the Concrete Laboratory of Universitas Islam Sumatera Utara (UISU), the determination of concrete quality is still conducted manually, relying on subjective experience, which can lead to inconsistencies in assessment. Therefore, developing a system based on the SAW algorithm is necessary to enhance efficiency and objectivity in selecting the best concrete. The research process begins with data collection on concrete samples, covering parameters such as compressive strength, water volume, setting time, cement content, and aggregate quantity. Each criterion is assigned a weight based on its importance, followed by normalization to align scale values. The SAW algorithm is then applied to calculate the final preference values for each concrete sample, ultimately generating a recommendation for selecting the highest-quality concrete. The study results show that Concrete C achieves the highest final score (0.94706), followed by Concrete A (0.88328) and Concrete B (0.76292). The study concludes that the SAW algorithm effectively enhances objectivity and accuracy in determining the best concrete quality.