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Rice plants (Oryza Sativa L.) are the main staple food commodity in Indonesia, as most of the Indonesian population relies on rice as their primary food. One of the causes of low rice production in Indonesia is that farmers generally cultivate rice improperly, such as in land preparation or land selection. Land suitability in rice cultivation greatly affects crop productivity. A process that can support decisions regarding rice land suitability is the development of a Decision Support System (DSS) website using a combination of the Simple Additive Weighting (SAW) method and the Technique for Order Performance of Similarity to Ideal Solution (TOPSIS). This combination is performed by taking the average (µ) of the final results from the SAW and TOPSIS methods. The final scores of each method are calculated separately, and then the average (µ) of these two results is taken to obtain the final ranking of the alternatives. The data used to determine the suitability of rice land is based on five criteria: soil type, soil pH, rainfall, temperature, irrigation and water supply. The alternative data used in the study includes six alternatives: Sungai Kunjang, Sambutan, Samarinda Utara, Palaran, Loa Janan Ilir, and Samarinda Seberang. The aim of this research is to provide information on alternative solutions to farmers or farmer groups in determining rice land suitability. The results of the combination of the SAW and TOPSIS methods show that the alternative with the highest final score is Samarinda Utara (A3), with a final score of 0.7337. Meanwhile, the alternative with the lowest final score is Sambutan (A2), with a final score of 0.4402.

The verification and validation process for land application data at the Southwest Sumba Regency Land Office has been carried out manually, which often results in delays, data inconsistencies, and the potential for errors in decision-making. These problems impact the low efficiency of public services and the accuracy of managed data. Therefore, this study aims to develop a Decision Support Sistem (DSS) that can assist the verification and validation process for land application data sistematically, quickly, and objectively. This sistem is built using the Simple Additive Weighting (SAW) method, which functions to determine the level of eligibility of an application based on several criteria, namely completeness of documents, land status, legality of ownership, suitability of applicant data, and dispute history. The sistem design process is carried out through several stages, including needs analysis, design, implementation, testing, and evaluation. The results show that the implementation of a SAW-based DSS can increase the efficiency of the verification process by up to 65%, and increase the accuracy of validation results by up to 90% compared to manual methods. This sistem provides automatic decision recommendations, thus assisting officers in assessing the eligibility of land applications in a transparent and consistent manner. Thus, this sistem has the potential to be an effective solution to support the digitalization of public services in the land sector in Southwest Sumba Regency.

Some schools currently still use manual methods for assessment in determining the best teachers.  Giving grades to teachers is sometimes still subjective, so the risk of conflict is high due to inaccurate and non-transparent assessments, making it a problem for teachers in teaching. For this reason, an information system is needed to get accurate calculation results to get the best teacher ranking. In answering the problems found, the author developed a decision support system using the Simple Additive Weighting method to obtain accurate ranking results so that it can provide maximum decision results through faster, more accurate and precise classification and evaluation. And this research uses a prototype method as an addition to make system development easier. The results of calculations using the SAW method can determine the ranking for the best teachers. Based on a comparison between the pretest and posttest results, it was found that the conformity level was 60% for appropriate data from the 10 data used and 40% for inappropriate data.

Bali is a renowned tourist destination that attracts visitors from around the world, particularly for its natural beauty, rich culture, and delicious cuisine. The increasing number of tourists in Bali has driven rapid growth in the culinary industry. In Denpasar City, selecting the right location is a key factor for the success of culinary businesses, as each location has different characteristics and potentials. This study employs the Multiple Attribute Decision Making (MADM) model, combining the Simple Additive Weighting (SAW) and Technique for Orders Preference by Similarity to Ideal Solution (TOPSIS) methods, to determine the optimal location for culinary businesses in Denpasar City. Data were collected through surveys of 154 culinary business owners, considering eight criteria: Accessibility, Visibility, Traffic, Facilities, Expansion, Environment, Competition, and Regulations. The study's findings indicate that both SAW and TOPSIS methods identify high population density areas as the best choice. The SAW and TOPSIS method provides the highest preference value of 0,8815 and 0.7082 respectively, making it the more effective method for recommending optimal culinary locations in Denpasar City.

Information systems now play a crucial role in various fields, including education. Yayasan Aldiana Nusantara (YAN), which supports students from low-income backgrounds, faces challenges in selecting scholarship recipients. To streamline this process, this research designs a web-based Decision Support System (DSS) using the Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The system considers criteria such as class, KPS/KIP/KKS recipients, parents' income, report card grades, and extracurricular activities. The DSS is developed using the Waterfall model, PHP, and MySQL, with four types of access rights: Administrator, Judge, Student, and Foundation Leader. Sensitivity testing shows that the SAW method is more responsive to changes in criteria compared to TOPSIS, with a sensitivity value of 2.98 for SAW and 0.022 for TOPSIS. These results indicate that SAW is more optimal in assisting YAN in effectively and efficiently selecting scholarship recipients.

The Bengkalis Regency Education Office (Disdik) provides Local Children's Scholarships to students in an effort to help students to alleviate the dependents of the Single Tuition Fee (UKT) in pursuing education. In determining the recipient of the Tempatan Children's Scholarship, the Education Office government carries out several stages manually such as collecting all the files of students who register, after which the Education Office checks the completeness of the student files one by one, gives a value for each predetermined criterion and manually calculates the value of the criteria. A decision support system for recipients of web-based Local Children's Scholarships is needed to help determine recipients of Local Children's Scholarships to be faster and more accurate. This system is made using the Simple Additive Weighting (SAW) method and Multi Attributive Border Approximation Area Comparison (MABAC) to facilitate the calculation and comparison of the value of each alternative in the system, by determining the ranking based on the largest value as the best alternative. The result of the application of the SAW and MABAC methods in the system is that the system displays alternative data sequentially based on rankings that can help in determining the recipient of the Local Children Scholarship. From 60 field data, the application of SAW and MABAC methods on this system has an accuracy of 83.3%. The development method used is the Extreme Programming method.

This study aims to find the highest value of criteria, sub-criteria and alternatives in prioritizing K3 strategies at PLN NP UP Muara Tawar to ensure and protect the workforce. Using the 5M approach (Money, Man, Machine, Methods and Milieu), 21 sub-criteria and 5 alternatives in order to prevent, overcome and minimize the occurrence of work accidents and their impacts by identifying, analyzing and controlling appropriately to improve K3 in the workplace. The Simple Additive Weighting (SAW) mixed method is used to calculate criteria and sub-criteria and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) calculates alternatives. The results of the analysis show that the highest criterion is man (M) with a score of 0.2113 and the highest sub-criteria are K3 Infrastructure Investment (MO1) and Fire Protection System Infrastructure Investment (MO2) with a score of 0.29577. The highest alternative is with a score of 0.7900 on maintenance of equipment and facilities (A2).    

This article discusses the development of a decision support system (DSS) for selecting the best tourist attractions in Surakarta City using the Simple Additive Weighting (SAW) method. The background of the research is the importance of choosing the right tourist attractions to enhance visitor satisfaction and support regional development. The SAW method was chosen for its ability to provide structured assessments based on multiple criteria. This study uses an Agile approach in system development, involving planning, analysis, design, implementation, and testing stages. The results show that the developed decision support system can effectively manage the criteria and alternatives for tourist attractions. The calculations using the SAW method indicate that Sheikh Zayed Mosque has the highest preference value, making it the best tourist attraction according to this system. Testing shows that the system functions as expected, with consistent calculation results between manual and automated methods.  

Placing appropriate Field Work Practices (PKL) for students is very important, because it can maximize the abilities and talents of each student so as to produce graduates who are ready to compete in the world of work. In choosing a place for PKL students at SMK Negeri 4 Malang, the problem that often occurs is a mismatch in students' needs for a PKL place. To overcome this problem, a computer system is needed in the form of a decision support system that can help students at SMK Negeri 4 Malang to choose the right PKL place for students. The decision support system developed in this research uses the Moora and Simple Additive Weighting (SAW) methods in deciding the placement of PKL students. Based on the final results of the decision support system in the form of ranking. Moora's advantages include being stable and strong, this method does not even require an expert in mathematics to use it and requires simple mathematical calculations. Moora also has a good level of selectivity because it can determine goals from conflicting criteria. Where the criteria can be profitable (benefit) or unprofitable (cost). The SAW method was chosen because this method determines the weight value for each attribute, then continues with a ranking process which will select the best alternative. Research is carried out by looking for weight values for each criterion, and then creating a ranking process that will determine the optimal alternative.

The evaluation of trainer performance plays a critical role in maintaining the quality and reputation of LKP Bina Karya Mandiri as a non-formal training institution. This study aims to assess trainer performance objectively using the Simple Additive Weighting (SAW) method as part of a Decision Support Sistem (DSS). A descriptive-quantitative approach was employed through a case study involving five trainers: Wasito, Diah Rita Sari, Ahmad Jayadi, Bayu Adi Guna, and Komarudin. Data were collected through observation and surveys based on four primary criteria: Material Mastery, Teaching Methods, Participant Satisfaction, and Discipline, each assigned a predetermined weight. The findings indicate that the SAW method effectively transforms performance data into a structured and interpretable ranking. Based on preference value calculations, Bayu Adi Guna achieved the highest score of 0.935, followed by Wasito, Komarudin, Ahmad Jayadi, and Diah Rita Sari. These results demonstrate that SAW is a reliable tool for supporting decision-making in trainer performance evaluation. The implications of this study suggest that training institutions can adopt SAW to enhance objectivity, transparency, and efficiency in performance assessments, thereby strengthening human resource management practices.

The decision support system as a tool for decision making, has now begun to be widely applied in various fields of life, not least in making a decision for the process of accepting new students at the MA Sabilurrasyad NW Barabaliused. With the decision support system (SPK) can help schools in the selection of new students. The method used in building a decision support system in this research is the Analytical Hierarchy Process (AHP). The method used to select new students is the Analytical Hierarchy Process (AHP) which functions to calculate the final score of each student in the school and the Simple Additive Weighting (SAW) serves to rank each student.The result of this study is that the combination of the AHP (Analytical Hierarchy Process) and SAW (Simple Additive Weighting) methods in determining new student admissions at the MA Sabilurrasyad NW Barabali School can be used. SPK (Decision Making System) can display information according to what the user inputs correctly.