Analyzing Criteria Count Impact on SAW and TOPSIS Stability in Decision Support Systems - Alif Catur Murti; Muhammad Imam Ghozali; Indra Lina Puta; Ali Ikhwan (UIN Sumatera Utara, 2025)
This study investigates how increasing the number of decision criteria (5-30) affects the ranking stability and computational efficiency of Simple Additive Weighting (SAW) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Previous studies compared these methods in domains such as scholarship selection and food assistance but did not examine how rankings evolve under greater complexity. Using a synthetic dataset of five fixed alternatives with multiple random seeds, results show that SAW is more prone to ranking fluctuations, while TOPSIS demonstrates greater stability. Kendall's Tau reveals variability across scenarios, and sensitivity tests confirm that agreement depends on data generation. Computationally, SAW exhibits quasi-linear growth in processing time (≈0.002-0.008 s), whereas TOPSIS remains efficient (≈0.002-0.004 s) with minimal variance. These findings highlight a context-dependent choice SAW offers simplicity in low-dimensional settings, while TOPSIS provides scalability and robustness for complex, high-stakes decision support.