<![CDATA[This research aims to evaluate the effectiveness and efficiency of the SAW (Simple Additive Weighting) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methods in decision support systems and to identify recent trends and innovations in the application of these two methods across various fields. The research methodology involves collecting and analyzing various academic sources, including journals and conference proceedings that discuss the SAW and TOPSIS methods and compare them with other methods in decision support systems. It compares the effectiveness and efficiency of SAW and TOPSIS based on criteria such as accuracy, complexity, and ease of implementation. Case studies are conducted in various fields to observe the real-world application and performance of the SAW and TOPSIS methods in practical situations. The research concludes that although SAW and TOPSIS each have their own strengths and weaknesses, recent innovations and adaptations keep both methods relevant and effective in various decision support system applications. The SAW method has proven to be efficient in terms of computational time and easy to implement, making it suitable for problems with many quantitative criteria. However, this method is less effective in handling criteria with highly varying levels of importance. On the other hand, the TOPSIS method provides more accurate results in situations with diverse criteria and has the ability to handle more complex data, although it requires more time and computational resources compared to SAW]]>
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