Definition and Weaknesses of the SAW Method

Oct 13, 2025 by wbrum.org 145 views

Introduction

In the realm of decision support systems (SPK), the Simple Additive Weighting (SAW) method is a widely used approach to aid in decision-making processes. This method, also known as the weighted addition method, relies heavily on the subjective determination of weight values by decision-makers. However, this subjective approach can lead to inaccuracies in the selection results. Therefore, it is essential to combine methods to produce more accurate decisions. In this article, we will discuss how the merging of the SAW method with the Analytic Hierarchy Process (AHP) can increase the accuracy of determination of values in decision making.

Definition and Weaknesses of the SAW Method

The SAW method operates by adding up all the criteria values that have been given weights by the decision maker. Unfortunately, this approach tends to produce weights that are not based on in-depth analysis, so the resulting decisions may be inappropriate and not selective. In other words, the accuracy of the final result is very dependent on how to determine the weight by individuals who can be biased or not objective. This subjectivity can lead to inconsistent and unreliable results, which can have significant consequences in various fields, such as business, engineering, and healthcare.

The Importance of Objective Decision Making

Objective decision making is crucial in today's complex and dynamic world. With the increasing amount of data and information available, it is essential to have a systematic and objective approach to decision making. The SAW method, while widely used, has its limitations, and its subjective nature can lead to inaccurate results. Therefore, it is necessary to adopt a more objective approach to decision making, which is where the Analytic Hierarchy Process (AHP) comes in.

Solution through the AHP method

To overcome limitations in the SAW method, researchers introduce the Analytic Hierarchy Process (AHP) method. AHP is a technique that relies on quantitative measurements to determine the weight of criteria through a comparison scale. This process is carried out by identifying relevant criteria and comparing them to obtain priority scale based on the interests of each criterion. The scale of interest used follows the time method, which allows determining the weight to be more systematic and objective. By using AHP, the resulting Eigen Vector value can be integrated into the SAW method as a weight value. This means that the weight used in SAW calculations is not only subjective, but has a strong basis based on criteria analysis.

Case Study: Selection of Drinking Water Depot in Medan Marelan

The merging of the SAW and AHP method was tested in a case study in the Medan Marelan area, which focused on the selection of refill drinking water depots. In this study, water quality data from several depots were collected and analyzed using the combined method. With AHP, water quality criteria such as cleanliness, taste, and pH are compared and assessed to produce objective weight. Then, this weight is used in the calculation of SAW to determine which depot has the best water quality. The results of the study show that with the merging of these two methods, the selection of drinking water depots is more accurate and reliable.

Benefits of the AHP-SAW Method

The AHP-SAW method offers several benefits over traditional SAW methods. Firstly, it provides a more objective approach to decision making, which reduces the risk of biased or subjective results. Secondly, it allows for a more systematic and structured approach to decision making, which can lead to more accurate and reliable results. Finally, it provides a strong foundation for sustainable decision making, which is essential in today's complex and dynamic world.

Conclusion

In conclusion, the merging of the Simple Additive Weighting (SAW) method with the Analytic Hierarchy Process (AHP) is a powerful approach to decision making. By combining the subjective nature of SAW with the objective approach of AHP, we can produce more accurate and reliable results. The case study in Medan Marelan demonstrates the effectiveness of this approach, and its benefits can be applied to various fields, such as business, engineering, and healthcare. Therefore, the integration of AHP in the SAW method should be considered in various other decision applications in the future.

Future Research Directions

Future research directions for the AHP-SAW method include:

Application in various fields: The AHP-SAW method can be applied to various fields, such as business, engineering, and healthcare. Future research can focus on exploring the effectiveness of this method in different fields.
Development of new criteria: The AHP-SAW method relies on the development of new criteria to determine the weight of each criterion. Future research can focus on developing new criteria that can be used in the AHP-SAW method.
Comparison with other methods: The AHP-SAW method can be compared with other decision making methods, such as the Simple Multi-Attribute Rating Technique (SMART) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Future research can focus on comparing the effectiveness of these methods.

Limitations of the AHP-SAW Method

While the AHP-SAW method offers several benefits, it also has some limitations. Firstly, it requires a significant amount of data and information, which can be time-consuming and resource-intensive. Secondly, it relies on the development of new criteria, which can be subjective and biased. Finally, it can be complex and difficult to understand, which can lead to inconsistent and unreliable results.

Conclusion

In conclusion, the AHP-SAW method is a powerful approach to decision making that offers several benefits over traditional SAW methods. By combining the subjective nature of SAW with the objective approach of AHP, we can produce more accurate and reliable results. The case study in Medan Marelan demonstrates the effectiveness of this approach, and its benefits can be applied to various fields, such as business, engineering, and healthcare. Therefore, the integration of AHP in the SAW method should be considered in various other decision applications in the future.

Q: What is the Simple Additive Weighting (SAW) method?

A: The SAW method is a widely used approach in decision support systems (SPK) that relies on the subjective determination of weight values by decision-makers. It operates by adding up all the criteria values that have been given weights by the decision maker.

Q: What are the limitations of the SAW method?

A: The SAW method has several limitations, including its subjective nature, which can lead to inaccurate and unreliable results. Additionally, it relies on the decision-maker's ability to determine the weight values, which can be biased or not objective.

Q: What is the Analytic Hierarchy Process (AHP) method?

A: The AHP method is a technique that relies on quantitative measurements to determine the weight of criteria through a comparison scale. It operates by identifying relevant criteria and comparing them to obtain priority scale based on the interests of each criterion.

Q: How does the AHP method overcome the limitations of the SAW method?

A: The AHP method overcomes the limitations of the SAW method by providing a more objective approach to decision making. It relies on quantitative measurements to determine the weight of criteria, which reduces the risk of biased or subjective results.

Q: What is the AHP-SAW method?

A: The AHP-SAW method is a combination of the SAW and AHP methods. It integrates the subjective nature of SAW with the objective approach of AHP to produce more accurate and reliable results.

Q: What are the benefits of the AHP-SAW method?

A: The AHP-SAW method offers several benefits, including a more objective approach to decision making, a more systematic and structured approach to decision making, and a strong foundation for sustainable decision making.

Q: Can the AHP-SAW method be applied to various fields?

A: Yes, the AHP-SAW method can be applied to various fields, including business, engineering, and healthcare. Its benefits can be applied to any field where decision making is critical.

Q: What are the future research directions for the AHP-SAW method?

A: Future research directions for the AHP-SAW method include application in various fields, development of new criteria, and comparison with other decision making methods.

Q: What are the limitations of the AHP-SAW method?

A: The AHP-SAW method has several limitations, including its complexity and difficulty to understand, the need for significant amount of data and information, and the reliance on the development of new criteria.

Q: Can the AHP-SAW method be used in real-world applications?

A: Yes, the AHP-SAW method can be used in real-world applications, including business, engineering, and healthcare. Its benefits can be applied to any field where decision making is critical.

Q: How can the AHP-SAW method be implemented in practice?

A: The AHP-SAW method can be implemented in practice by following a structured approach, including identifying relevant criteria, comparing them to obtain priority scale, and integrating the results with the SAW method.

Q: What are the implications of the AHP-SAW method for decision making?

A: The AHP-SAW method has significant implications for decision making, including a more objective approach, a more systematic and structured approach, and a strong foundation for sustainable decision making.

Q: Can the AHP-SAW method be used in combination with other decision making methods?

A: Yes, the AHP-SAW method can be used in combination with other decision making methods, including the Simple Multi-Attribute Rating Technique (SMART) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS).

Q: What are the future prospects of the AHP-SAW method?

A: The future prospects of the AHP-SAW method are promising, with its benefits being applied to various fields, including business, engineering, and healthcare. Its use in combination with other decision making methods is also expected to increase.