A Comparative Study of Three Decision Support Methods: Proving Consistency in Decision-Making with Identical Inputs

Asyahri Hadi Nasyuha — 2025-06-12

Decision-making in complex environments often requires evaluating multiple alternatives against various criteria, which can sometimes result in inconsistent outcomes when different decision support methods are employed. Such inconsistencies pose significant challenges for decision-makers in determining the most reliable methodology. To address this gap, the present study examines whether three widely adopted decision support methods, Simple Additive Weighting (SAW), Simple Multi-Attribute Rating Technique (SMART), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), produce consistent results when applied to identical input values, criteria, and alternatives. The primary aim is to explicitly assess the consistency of decision-making outcomes across these methods under controlled conditions. The evaluation was conducted using a set of alternatives, with A1 consistently emerging as the top choice. Specifically, the SAW method produced a final score of 0.8998 for A5, the SMART method assigned a value of 0, and the TOPSIS method yielded a closeness coefficient of 0.826 for the same alternative. The unique contribution of this study lies in its systematic, side-by-side comparison of SAW, SMART, and TOPSIS using precisely the same dataset, an approach seldom addressed in prior research. By empirically demonstrating that these methods generate identical rankings under strictly controlled scenarios, this research provides new evidence supporting the methodological robustness and practical interchangeability of these widely used decision support techniques. The findings underscore the reliability of these methods in facilitating objective decision-making and offer valuable guidance for researchers and practitioners in selecting the most suitable DSS method without concern for inconsistent results.

Optimizing Socioeconomic Features for Poverty Prediction in South Sumatera

Terttiaavini Terttiaavini — 2025-06-12

Poverty in South Sumatera remains a complex challenge influenced by socioeconomic factors. Traditional methods often fail to capture nonlinear relationships critical for accurate prediction. This study enhances poverty prediction by optimizing feature engineering using 32-variable socioeconomic data from South Sumatra for the years 2019 to 2023. Data preprocessing included cleaning, imputation, normalization, and outlier handling. Feature aggregation created composite indices: Education Index (P1, P2, P3), Health Index (AH1–AH4), Economic Index (IE, GR, AI, EG), and Healthcare Workforce Index (HW1–HW9). Feature interaction derived ratios such as Income vs. Economy (AN/Education Index), Infrastructure vs. Health (road length/Healthcare Workforce Index), and Unemployment vs. Workforce (HI/AT), highlighting interdependencies. Dimensionality reduction (PCA) and Lasso Regression selected eight key predictors, including Year and Poverty Level. Among tested models, Random Forest performed best (R²=0.7244, MAE=0.2489). SHAP analysis identified Education and Economic Indices as top predictors. Optimized feature engineering improved model accuracy and interpretability, supporting targeted poverty reduction strategies in South Sumatera.

TIERS Information Technology Journal

A Comparative Study of Three Decision Support Methods: Proving Consistency in Decision-Making with Identical Inputs

Optimizing Socioeconomic Features for Poverty Prediction in South Sumatera