Computerized DSS for evaluating design performance of residential buildings using additive weighting approach

This study aims to design and implement a Decision Support System (DSS) based on the Simple Additive Weighting (SAW) method in the process of selecting the best supplier within the Micro, Small, and Medium Enterprises (MSMEs) environment, with a case study on Thaiyo Thaitea. In the face of increasingly competitive business environments, choosing the right supplier is a crucial factor in maintaining operational efficiency and service quality. However, the supplier selection process is often subjective and unstructured. Therefore, the SAW method is implemented to provide an objective and systematic approach to evaluating multiple supplier alternatives based on various criteria. This study uses five main criteria in supplier evaluation: price, product quality, completeness, delivery timeliness, and service. The SAW method allows for weighting and normalizing these criteria, resulting in a quantitative ranking of suppliers. The research findings indicate that supplier H. Slamet (A1) achieved the highest score (12.01), making it the best choice for Thaiyo Thaitea. System validation was conducted using the Black Box method to test the application's reliability and functionality. The test results demonstrate that all core features, including criteria data management, assessment processes, and user authentication, function as expected. This study provides practical contributions to improving efficiency and objectivity in the decision-making process for MSMEs. The study's implications indicate that the implementation of the SAW method in DSS can produce more transparent, accurate, and structured decisions. This research opens opportunities for further development through real-time data integration or comparisons with other Multi-Criteria Decision Making (MCDM) methods to enhance decision-making flexibility and accuracy in the future.

PT. Telkom Akses (PTTA) has constraints in the employee leave application management system, this is because the employee leave application process is still done manually and difficulties in determining the level of importance for employees who apply for leave. Therefore, a web-based application was built using FMCDM and the SAW Method. This study aims to improve efficiency and effectiveness in managing the employee leave application system by utilizing the Fuzzy Multi-Criteria Decision Making (FMCDM) approach using the Simple Additive Weighting (SAW) method at PT. Telkom Akses (PTTA). In order to make it easier for employees to carry out the deduction process and obtain complete and detailed information and rules related to deductions, the personnel department is expected to be able to create an online information system. To help with this, one of the fields of technology that can be utilized is soft computing technology. There are various methods that can be used to solve problems, one of which is the Fuzzy Multi Criteria Decision Making (FMCDM) method and the Simple Additive Weighting (SAW) method. The results of the study indicate that the web-based application that has been designed can help solve the problems faced by PT. Telkom Akses Medan STO Simpang Limun in terms of managing the employee leave application system and helping to determine the level of importance and ranking for employees who apply for leave accurately and efficiently, and the results of the application of FMCDM and the SAW Method in determining employee leave permits are that out of 7 employees who apply for leave, 2 employees get the highest rating results. The conclusion of this study is to help decision makers in determining employees who get leave permits based on relevant and transparent assessments of each employee who applies for leave.

The purpose of this research is to analyze and apply the Simple Additive Weighting (SAW) method in the context of determining motor oil brands, collect and process data on motor oil alternatives available on the market and evaluate the results of motor oil selection using the SAW method. This research identifies research variables adopted based on motorcycle engine oil criteria using the Simple Additive Weighting (SAW) method. There are four variables that form the basis of evaluation to determine the most popular shockbreaker brand: Size (C1), Type (C2), Viscosity (C3), Price (C4). The results of the study concluded that through the integration of the SAW method, researchers succeeded in building a comprehensive framework for evaluating relevant criteria in oil selection. The test results show that SAW is able to provide relative weights for each criterion used in this study and successfully rank oil alternatives based on the normalized value of each criterion. Based on the calculation results shown in the decision support system application, the first recommendation is Enduro Matic V followed by Yamalube Engine Oil Super Matic with a value of 1 and Shell Advance AX7 Scooter at rank 2 with a value of 0.96 and rank 3 AHM Oil Matic SPXX2 with a value of 0.863. The application that has been designed has a fairly good page display so that it provides convenience to users.

The recruitment process for video editing staff is a strategic stage in ensuring the quality of professional and competitive content production. However, candidate assessment often faces challenges of subjectivity and inaccuracy in decision-making when evaluators rely solely on intuitive judgment without a measured approach. This study aims to develop a decision support system based on Multi-Criteria Decision Making (MCDM) by integrating the Entropy method for objective determination of criteria weights and the Simple Additive Weighting (SAW) method in calculating the preference values of alternatives. Five evaluation criteria are used in the selection process, namely Editing, Creativity, Experience, Discipline, and Teamwork, with the final weights obtained through the Entropy method being 0.2867, 0.2248, 0.2573, 0.0685, and 0.1626. The study results show that the SAW method is capable of processing candidate evaluation scores comprehensively based on these weights, producing final scores that indicate the best candidates, namely Eko Firmansyah (0.986), Indra Mahendra (0.9699), and Candra Wijaya (0.9662) as the three candidates with the highest eligibility. This study demonstrates that the integration of the Entropy–SAW method is effective in creating a selection mechanism that is objective, transparent, and scientifically accountable, thus making a significant contribution to decision-making in the field of human resource management

In today's modern era, cafes serve a variety of food and beverage menus to their customers. However, this high diversity often leads to problems in selecting menus that suit customer preferences. The main problem faced is the difficulty for customers in choosing the menu that best suits their personal tastes and preferences, given the large number and variety of menus offered by the cafe. The purpose of this research is to find a solution to the problem of selecting a cafe menu using the Simple Additive Weighting (SAW) method. The SAW method is applied to analyze cafe menu data and provide the most suitable menu recommendations based on individual preferences. There are 6 criteria applied in this study to match customer preferences, namely price, portion size, level of popularity, quality of ingredients, compatibility with taste, and aesthetic aspects. It is hoped that this research can provide better guidance for cafe customers in choosing a menu that suits their tastes, as well as help cafe owners in increasing customer satisfaction and sales. The results of calculations using the SAW method that has been carried out get the results of 5 menus that become recommendations because they get the top rank, first Alternative A1 menu Oatmeal Protein Shake has the highest rank with a score of 0.86, second alternative A7 menu Pepperoni Pizza with a score of 0.847, third alternative A4 menu Ramen Noodle with a score of 0.813. fourth alternative A1 menu Alfredo Spaghetti with a score of 0.766 and the fifth Alternative A8 menu Seafood Fried Rice with a score of 0.738.

Currently, the need for an internet connection is increasing due to the human need for information and communication that can be obtained through the internet. A decision support system with criteria is needed to solve the challenge of choosing an internet package with many components or criteria that must be evaluated. Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) are two multi-criteria decision support system techniques, but they use different measurement techniques. This research aims to identify the best Internet Service Provider by applying the Simple Additive Weight (SAW) and Analytic Hierarchy Process (AHP) techniques. The objects used in this research are the providers Indihome, Biznet, and Solnet. The weight of each criterion is determined using the AHP technique, and the best suppliers are ranked using the SAW approach. The sampling technique used in this research was Purposive Sampling. In this study, the ranking results of 100 respondents showed that the ranking for the AHP method was 60% and for the SAW method was 92%. Thus, global calculations using the AHP and SAW methods were obtained for the best alternative ISP, namely Indihome.

A pharmacy is a location where pharmaceutical activities are carried out and the distribution of drugs to the public. This research focuses on the problem of managing drug inventory at Arto Moro Pharmacy, which is based on the need for an efficient decision support system in determining the optimal amount of drugs. Issues such as drugs that are no longer suitable for use and waste require a more efficient management system. The purpose of this research is to develop a decision support system using the Simple Additive Weighting (SAW) method to overcome these problems. The SAW method is used to handle problems in complex decision-making by combining relevant criteria and giving an assessment of each criterion. The research results consist of four main stages: the creation of a decision matrix from drug demand and supply data, the creation of a decision matrix for each type of drug, the calculation of rankings by summing the criteria matrix, and the determination of the amount of inventory for May by considering the final value in the form of a percentage of the previous moth’s demand (April). The application of the SAW method has succeeded in creating an effective decision support system in determining the amount of drug inventory at Arto Moro Pharmacy. The results of this study provide an in-depth understanding of the application of the SAW method in drug inventory management in pharmacies, which contributes to addressing the problem of efficiency.

Style Queen Kebaya Store (SQ Kebaya) is a store that is engaged in apparel, its product sales focus includes adult and children's kebaya. The negative impact of the Covid 19 Pandemic has proven that the Store (SQ Kebaya) has experienced a decline in sales turnover in 2020, therefore the SQ Kebaya Store's efforts to restore its sales activities are by giving gifts for customer appreciation during the COVID 19 season through selecting the best customers for the 2020 period. However, the problem faced by SQ Kebaya Stores in the process of evaluating the best customer selection is that there is no criterion weight so that the decision making is not right on target, making the best customer decisions less efficient because they have to look for customer sales records manually in the sales record book. This study produces a web-based decision support system for selecting the best customers at SQ Kebaya Stores using the AHP (criteria weight), SAW and WASPAS (best customer ranking) methods, this study produces priority weights and importance levels of each criterion, namely status (0.37 ), method of payment (0.23), total spending (0.14), quantity (0.13), intensity of visits (0.07), length of subscription (0.07) and the result of ranking the percentage of the largest alternative value is the alternative SAW method with an average of 0.6952 , while the WASPAS method is 0.6405. It can be concluded that the right method used to obtain the best alternative value is the SAW method.

<p>Road infrastructure is a primary transportation asset extensively used by society to support economic, educational, and other activities. Consequently, roads function as a crucial supporter of the country's socio-economic activities. UU No. 38 of 2004 regarding Roads stipulates that roads are essential transportation facilities for the economy, social aspects, environment, politics, and defense and security. This research employs the Simple Additive Weighting (SAW) method to collect secondary data. The secondary data is obtained from the Department of Communication, Informatics, and Statistics of Lampung Province and processed to generate an assessment of road segments based on preference values. The research results indicate that, based on the criteria weighting, heavily damaged road conditions have the highest importance weight at 58.6%, followed by the criterion of average daily traffic. These findings suggest that road experts share a similar perspective on how road criteria are prioritized for specific regions. The ranking results using the SAW method with a Likert scale indicate a value of 97.34.</p>

As an appreciation to their customer, PT Pinjam Indonesia has a Gold product. Gold is a product that allows customer to get 10% discount of total bill every 6 months, and is given to two customers. Until now, PT Pinjam Indonesia has difficulties in deciding which customer from Online mortgage system deserves to be rewarded with the Gold product, this problem occurs because of the many numbers of customer in the system. Therefore, Decision Support System is needed to help speed up and make the deciding process easier. Decision Support System decide the highest alternative value that deserves to receive the Gold product using the Simple Additive Weighting (SAW) method wtih prescribedcriterias. The basic concept of Simple Additive Weighting (SAW) method is to find the weighted additive from performance rating of every alternative’s attributes. SAW method needs a normalization process of the decision matrix to a scale that can be compared with every rated alternatives. A calculation process is applied to all the criterias to get the result of which alternative is best to receive the Gold product. This method could help the decision making process to be more objective.

In accordance with the regulations set by the Office of Kotasan Village, residents are entitled to receive subsidized food based on specific criteria. To determine who is selected to receive subsidized food and distribute it to low-income families will be conducted by the Kotasan Village Office. As a supporting tool to determine individuals eligible to receive subsidized food, a decision support system is required. Within the decision support system, there are several methods, one of which can be used is the Simple Additive Weighting (SAW) method. In this study, the author will address a case to find the best alternative from certain established criteria by comparing the alternatives using the Simple Additive Weighting (SAW) method. In this evaluation, the alternative Saidi ranks first with a Vi value of 0.910, followed by Zum in second place with a Vi of 0.820. In third position, Arju has a Vi value of 0.761. Meanwhile, the alternative with the lowest rank is Sri with a Vi of 0.425, followed by Supri with a Vi of 0.433 and Paiman with a Vi of 0.461.

The selection of educational assistance recipients is an important process that determines the effectiveness of aid distribution. However, inconsistencies in assessment criteria and less systematic data management often become obstacles in determining the right recipient candidates. This problem results in subjectivity and lack of transparency in the selection process. This study proposes a solution in the form of implementing the Simple Additive Weighting (SAW) method as a multi-criteria-based decision support system. This method is used to process data on prospective recipients with criteria including economic conditions, number of family dependents, written test results, and interviews. The approach used is quantitative descriptive with stages of data collection, criteria weighting, SAW score calculation, and evaluation of results. The results of the study show that the SAW method is able to provide objective and consistent rankings of prospective recipients. Evaluation of real data on scholarship recipients shows an accuracy level of 84.62%, indicating the effectiveness of this method in the selection process. These results indicate that the SAW method can be an effective solution to increase transparency, consistency, and fairness in the educational assistance selection process.

The cost of education at tertiary institutions in Indonesia is relatively high for some groups of people. Indonesian government agencies are trying to reduce this problem. One of the ways is by providing scholarships. The large number of scholarship enthusiasts becomes a challenge for the scholarship manager in making decisions on scholarship recipients. Scholarship selection at UIN Ar-Raniry is undertaken manually, so it is less efficient in making decisions on scholarship recipients. Hence, Decision Support System is required in the selection of scholarship recipients so that it fits with the criteria. With the existence of predetermined criteria, the Simple Additive Weighting (SAW) method is employed in which this method is simple, the algorithm is not difficult, and produces precise and accurate analysis based on the specified criteria values and preference weights. This study aims to analyze the SAW algorithm in selecting KIP scholarships at UIN Ar-Raniry so that it can facilitate the selection process. This test is carried out by calculating the weight of the SAW method with the concept of the weighted sum of each alternative and all its attributes. Calculation of the best value ranking results obtained from all the alternatives used that are eligible to become KIP-College scholarship recipients. Hence, it can be concluded that by applying the SAW method in determining scholarship acceptance at UIN Ar-Raniry, it can help facilitate the selection of scholarships efficiently and effectively.

Selecting competent teachers plays a vital role in enhancing the quality of education at Pangkalpinang Baptist School. However, the use of subjective judgment in the recruitment process often leads to less effective decision-making. Therefore, this research aims to examine the teacher selection mechanism by applying the Simple Additive Weighting (SAW) method to strengthen the objectivity and precision of the selection decisions. The SAW approach assesses applicants using three main criteria: interview performance (30%), academic test results (35%), and micro teaching skills (35%).Each criterion receives specific weights according to importance levels, followed by calculations to determine candidates with the highest scores. Research results demonstrate that SAW method implementation provides more systematic and transparent decisions in teacher selection. The study evaluated 10 teacher candidates with Eka Sitompul achieving the highest score of 0.85, followed by Fandi Saputra and Vitta Natalia with 0.825 each. This method enables schools to conduct data-based selection, reducing subjectivity in recruitment processes and ensuring selected teaching staff possess competencies aligned with school requirements.

Dinas Koperasi, UKM, dan Perdagangan Tanah Laut conducts the health of Koperasi by manually checking the financial report data of each Koperasi. It takes precision and a long time to carry out Koperasi health checks with manual calculations. This study aims to determine the best Koperasi performance using a decision-making system with the Simple Additive Weighting (SAW) method. The criteria to determine the best Koperasi performance were based on the attributes of the governance, risk profile, financial performance, and capital of Koperasi. The SAW method was used to select the best Koperasi by adding up each attribute, then multiplying by the weight of the related attributes. Based on the calculations using the SAW method, Koperasi 33 was selected as the best Koperasi with the highest vector value (V(i ))= 0.944719. This means that Koperasi 33 can be categorized as the best Koperasi