Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Advance Sustainable Science, Engineering and Technology (ASSET)
Nugroho Dwi Saputro
Universitas Persatuan Guru Republik Indonesia Semarang
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Scalable TOPSIS Variants in Web-Based Decision Support Systems: A Performance Benchmarking Study on Vectorization and Uncertainty Modelling Ghufron Abdullah; Nugroho Dwi Saputro; Nurkolis
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.3122

Abstract

This study systematically benchmarks the computational performance and decision consistency of three TOPSIS variants—classical TOPSIS, vectorized TOPSIS, and Z-TOPSIS—in scalable web-based multi-criteria decision support systems (DSS). Although TOPSIS is widely used due to its simplicity and interpretability, its scalability and computational behavior under concurrent web workloads remain insufficiently explored. To address this gap, the algorithms were evaluated using datasets containing 50–500 alternatives and 10 criteria, representing realistic decision-making scenarios. Classical TOPSIS was used as the baseline, vectorized TOPSIS applied matrix-based optimization, and Z-TOPSIS incorporated Z-numbers to capture uncertainty. Experiments were conducted on a cloud-based DSS equipped with multi-core CPUs and 16–32 GB RAM, measuring execution time, throughput, response time, and ranking consistency under workloads of 50–500 concurrent users. The results show that vectorized TOPSIS reduced execution time by approximately 50–55% (26.3 ms vs. 57.9 ms) and achieved the highest throughput of 480 requests per second. In contrast, Z-TOPSIS produced higher latency (68.4 ms) due to additional reliability computations. Ranking consistency remained high between classical and vectorized TOPSIS (Kendall’s tau ≥ 0.98), while Z-TOPSIS showed minor deviations (tau = 0.91). These findings provide practical guidance for selecting TOPSIS variants in scalable web-based MCDM applications.
Co-Authors Ghufron Abdullah Nurkolis