Simple Additive Weighting Method for Improving Decision Support Systems Laptop Selection - Ika Riantika; Martanto; Arif Rifaldi Dikananda; Ahmad Rifai (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2025)
The development of information technology significantly benefits various activities, particularly for students, by facilitating access to information and supporting academic tasks. However, students majoring in Information Technology often face challenges in selecting a suitable laptop due to the wide range of options with varying specifications and prices. This study aims to develop a Decision Support System (DSS) based on the Simple Additive Weighting (SAW) method to assist in choosing the best laptop. The SAW method was selected for its ability to evaluate multiple criteria through a weighting process. The study utilizes five main criteria: price, processor, RAM, storage type, and storage capacity. Data were collected through interviews and observations at the "IComp" laptop store. The analysis process involves matrix normalization and preference value calculation to determine recommendations. The DSS recommends the best laptop based on the highest preference score: Lenovo IP Flex 5 (0.78), followed by Lenovo IP3 (0.77) and HP Pav14 (0.76). The results indicate that these laptops offer an optimal balance between performance and price. The web-based sy stem designed accelerates the evaluation process, enhances objectivity, and improves user accessibility. The implementation of the SAW method proves effective and accurate in determining the best laptop, particularly in scenarios combining cost and benefit criteria. The system successfully meets the needs of Information Technology students by providing relevant and reliable results. This study successfully develops a DSS using the SAW method for selecting the best laptop. The system designed is effective and reliable for multi-criteria decision-making. Future research can integrate real-time data and broader user surveys to improve result generalization, making it applicable to other product selection contexts.