Kombinasi Metode SWARA dan Simple Additive Weighting (SAW) Pemilihan Tempat Kursus
At a Glance
Section titled “At a Glance”| Metadata | Details |
|---|---|
| Publication Date | 2023-12-01 |
| Journal | Journal of Artificial Intelligence and Technology Information (JAITI) |
| Authors | I Wayan Sriyasa |
| Analysis | Full AI Review Included |
Executive Summary
Section titled “Executive Summary”This research details the implementation of a hybrid Multi-Criteria Decision Making (MCDM) system, combining SWARA (Step-wise Weight Assessment Ratio Analysis) and SAW (Simple Additive Weighting), to objectively rank and select the best course venue.
- System Goal: To provide a transparent and accurate method for selecting the optimal course venue by balancing qualitative and quantitative criteria.
- Weighting Mechanism: The SWARA method was utilized to determine the relative importance (weights) of six criteria based on decision-maker preferences.
- Key Criteria Weights: Cost (PTK-1), Facilities (PTK-3), Reputation (PTK-5), and Instructor Qualifications (PTK-6) received the highest normalized weight (Wj) of 0.1875.
- Ranking Mechanism: The SAW method aggregated the normalized performance scores (Rij) of four alternatives using the SWARA-derived weights to produce a final ranking score (Vi).
- Top Performance: The Victoria venue achieved the highest final aggregated score (Vi) of 0.925, securing Rank 1.
- Methodological Advantage: The combination leverages SWARA’s strength in structured criteria weighting and SAW’s simplicity and interpretability in final alternative ranking.
Technical Specifications
Section titled “Technical Specifications”The following table summarizes the key parameters and results derived from the SWARA-SAW MCDM model.
| Parameter | Value | Unit | Context |
|---|---|---|---|
| Decision Model | SWARA + SAW | N/A | Hybrid Multi-Criteria Decision Making |
| Total Criteria (C) | 6 | N/A | PTK-1 (Cost) to PTK-6 (Benefit) |
| Total Alternatives (A) | 4 | N/A | Victoria, Anisya, Berlian, Zalia |
| Highest Criterion Weight | 0.1875 | Ratio | Assigned to Cost, Facilities, Reputation, Instructor Qualifications |
| Lowest Criterion Weight | 0.125 | Ratio | Assigned to Location and Teaching Equipment |
| Normalization Method | Max/Min Scaling | N/A | Rij calculation (Benefit: Xij / max(Xij); Cost: min(Xij) / Xij) |
| Rank 1 Alternative | Victoria | N/A | Highest Vi score |
| Rank 1 Final Score (Vi) | 0.925 | Vi Score | Aggregated weighted performance |
| Rank 2 Final Score (Vi) | 0.9047 | Vi Score | Berlian alternative performance |
| Lowest Cost Input (PTK-1) | 1,750,000 | Currency (IDR) | Victoria alternative |
Key Methodologies
Section titled “Key Methodologies”The selection process utilized a structured, iterative approach involving criteria definition, weight derivation (SWARA), normalization, and final ranking (SAW).
- Criteria Definition: Six criteria (PTK-1 to PTK-6) were established based on a comprehensive needs analysis, covering financial (Cost) and quality/infrastructure (Benefit) factors.
- Initial Subjective Weighting (Sj): Experts assigned initial subjective weights (Sj) to criteria, reflecting their perceived importance (e.g., S2 = 2, S3 = 1).
- SWARA Coefficient Calculation (Kj): The coefficient Kj was calculated based on the subjective weight (Kj = Sj + 1 for j > 1; K1 = 1).
- SWARA Relative Weight Calculation (qj): Intermediate relative weights (qj) were calculated iteratively (qj = qj-1 / Kj). The sum of these relative weights (Σqk) was determined to be 5.333.
- SWARA Final Normalized Weight (Wj): The final criteria weights were normalized by dividing each qj by the total sum (Wj = qj / Σqk), ensuring ΣWj = 1.
- SAW Normalization (Rij): Raw data (Xij) for the four alternatives were normalized. Cost criteria were normalized inversely (min/Xij), while Benefit criteria were normalized directly (Xij/max).
- SAW Final Scoring (Vi): The final performance score (Vi) for each alternative was calculated by summing the product of the normalized score (Rij) and the SWARA weight (Wj): Vi = Σ [Wj * Rij].
- Ranking: Alternatives were ranked based on the descending order of their final Vi scores.
Commercial Applications
Section titled “Commercial Applications”The SWARA-SAW methodology is a robust tool applicable far beyond educational selection, offering structured decision support in complex engineering and manufacturing environments.
- Materials Selection: Prioritizing candidate materials for a specific application (e.g., high-temperature alloys, composite matrices) based on weighted factors like cost, yield strength, corrosion resistance, and density.
- Equipment Procurement: Ranking competing capital equipment (e.g., CVD systems, lithography tools, metrology instruments) based on throughput, precision, maintenance cost, and footprint.
- Process Optimization: Selecting the optimal operating conditions (temperature, pressure, flow rate) for a chemical or physical process by weighting outputs such as efficiency, purity, and energy consumption.
- Infrastructure Investment: Evaluating potential sites for new industrial facilities or data centers based on weighted criteria including power availability, seismic risk, and logistical access.
- 6ccvd.com Relevance (Hypothetical): This methodology could be used to select the best precursor chemical supplier for CVD processes, weighting criteria such as purity level, delivery reliability, and unit cost.
View Original Abstract
The combination of SWARA and SAW methods in course venue selection is a comprehensive approach to determining the best course place. The SWARA method is used to identify the relative weights of each criterion in the selection of course venues, with stepwise evaluation steps to produce accurate weight ratios. Furthermore, the SAW method is applied to give a weighted value to each alternative course place based on predetermined criteria. By combining the advantages of SWARA in criteria weighting analysis and SAW in alternative ranking, this approach provides more accurate and transparent results in choosing the course place that best suits the needs and preferences of users. The ranking results showed that rank 1 with a total value of 0.925 was obtained by Victoria, rank 2 with a total value of 0.9047 was obtained by Diamond, rank 3 with a total value of 0.8708 was obtained by Anisya, and the last rank with a total value of 0.8531 was obtained by Zalia.