Systematic Literature Review: Analisis Pendekatan Matematis dalam Sistem Pendukung Keputusan

Abstract

This study aims to analyze mathematical approaches used in Decision Support Systems (DSS) through a Systematic Literature Review (SLR) method. A total of 41 scientific articles published between 2021 and 2026 were selected based on predefined inclusion criteria. The SLR process was conducted through several stages, including research question formulation, literature search, study selection, data extraction, and synthesis of findings. The results indicate that mathematical approaches in DSS can be classified into four main categories: Multi-Criteria Decision Making (MCDM), probabilistic, optimization, and hybrid approaches. Based on the distribution of articles, hybrid approaches are the most dominant with 19 articles, followed by MCDM with 10 articles, probabilistic with 7 articles, and optimization with 5 articles. The dominance of hybrid approaches reflects a paradigm shift from single-method usage toward the integration of multiple mathematical approaches to enhance system flexibility and accuracy. MCDM approaches remain widely used in structured multi-criteria problems, while probabilistic approaches play an important role in handling uncertainty and dynamic systems. Meanwhile, optimization approaches are applied to determine optimal solutions based on objective functions and constraints. This study contributes to systematically mapping and classifying mathematical approaches in DSS, as well as identifying research trends and future directions. The findings are expected to serve as a reference for developing more adaptive and integrated decision support systems.