Decision Support System For Selection Research Articles

Novel adaptive parameter fractional-order gradient descent learning for stock selection decision support systems

Novel adaptive parameter fractional-order gradient descent learning for stock selection decision support systems

Implementation of Fuzzy Logic on the Feasibility of Duck Egg Quality

Eggs are a staple food, with chicken eggs being the most common choice. However, duck eggs are gaining popularity due to their nutritional value. Ducks, a local poultry breed, are seeing increased interest, leading to a growth in the national duck population. Duck eggs themselves are one source of animal protein that is rich in nutrients and easily digested. Duck eggs have a very high protein content, which is 13.1 grams per 100 grams. The purpose of building a decision support system for duck egg selection is to help people choose quality duck eggs for consumption. Data collection techniques in this study use literature studies and include direct calculations and using MATLAB software to develop a Fuzzy logic system in determining the feasibility of duck egg quality. The results showed that in the example case for a duck egg that weighs 5.5 grams and then the thickness of the shell is 7.5 mm and the viscosity of the egg contents is given a number of 7, then the egg is still said to be suitable for consumption. This contributes to improving food quality in Indonesia by classifying the feasibility of eggs for public consumption.

Fuzzy set-based decision support system for hydrogen sulfide removal technology selection in natural gas processing: a sustainability and efficiency perspective.

Removing hydrogen sulfide (H2S) toxic and corrosive gas from the natural gas processing and utilization industry is a challenging problem for managers of these industries. This problem involves different economic, environmental, and health issues. Various technologies have been employed to remove the H2S gas from these industries, and choosing appropriate H2S removal technologies is a complex multi-criteria decision-making (MCDM) problem. In this regard, the present research work aims at developing a novel decision support system (DSS) by integrating MCDM techniques based upon the (best worst method) BWM and (alternative ranking order method accounting for two-step normalization) AROMAN methods under the decomposed fuzzy set theory. The DSS is used to assess technologies for removing H2S gas and then choose the best scenario for implementing among these technologies. Fourteen criteria in four environmental, social, economic, and efficiency aspects and five technologies of fixed-bed activated carbon technology (A_h2s1), biotrickling filter technology (A_h2s2), scavenger technology (A_h2s3), chemical oxidation scrubber technology (A_h2s4), and liquid redox technology (A_h2s5) have been chosen for evaluation. The final value of each technology is obtained as A_h2s1 (0.5550), A_h2s2 (0.5262), A_h2s3 (0.5809), A_h2s4 (0.6357), and A_h2s5 (0.5790). The results reveal that the chemical oxidation scrubber technology (A_h2s4) and biotrickling filter technology (A_h2s2) with the values of 0.6357 and 0.5262 are respectively the best and worst scenario for the removal of H2S produced by the natural gas processing and utilization plants of Iran. Therefore, the DSS is feasible and applicable and gives reliable and robust findings for acquiring the optimal H2S removal technology.

Sistem Pendukung Keputusan Pemilihan Kayu Dalam Konstruksi Atas Rumah Dengan Aspek Kualitas Dan Keberlanjutan Menggunakan Metode Weighted Sum Model Studi Kasus Pada CV Persada Sejahtera Kabupaten Ponorogo

House constuction is one of the main aspects of infrastructure development in various parts of the world. A house is not only a residence for individuals and families, but also reflects environmental sustainability and the cultural values of the local community. Therefore, selecting the right construction materials is crucial in ensuring the success of an efficient and sustainable home building project. The aim of this research is to design and build a decision support system for wood selection in the construction of house structures with quality and sustainability aspects using the Weighted Sum Model method. The decision support system using the Weighted Sum Model method was chosen because in its solution the ranking results of the WSM method are objective, so that it can provide fairness in decision making and can provide better results than conventional methods. The results of testing the DSS application using Black Box Testing, based on this test, the results obtained were as expected and valid. In this research, the user role is only for admin, for future researchers it can be developed by adding roles for users such as roles for company owners.

Development of a material selection decision support system for an automotive application

This study presents the development of a material selection decision support system for an automotive rooftop tent. The system uses five parameters of material cost, formability, corrosion resistance, tensile strength and hardness to optimize material choice from a list of aluminium alloy options. This was done before production via the deep drawing process can commence. The parameters were quantified using the average market prices for each alloy, product requirements obtained from the case study and the performance data that was obtained from the uniaxial tensile and hardness tests. These inputs were combined with the developed material selection framework and then programmed into a web-based decision support system for automated, data-driven material selection. The output presents the recommendation for the optimum material that balances cost-effectiveness and performance. The decision support system was successfully used to select the optimum alloy for the rooftop tent. Results show that AA1050 is the optimum material, providing weight reduction at the minimum achievable cost without compromising the product requirements for the rooftop tent. The decision support system is also scalable, and this allows it to be used with larger datasets for different products and processes in the future.

Decision Support System For Food Menu Selection For Boarding Students Using The Fuzzy AHP Method

Decision Support System For Food Menu Selection For Boarding Students Using The Fuzzy AHP Method

Enhancing decision support system for finished vehicle logistics service provider selection through a single-valued neutrosophic Dombi Bonferroni-based model

Finished vehicle logistics (FVL) activities are executed through agreements with service provider firms. Vehicle manufacturers make efforts to select the most successful FVL service provider. The motivation for this research lies in developing and demonstrating the application of a decision support system for FVL service provider selection. Within this scope, it is necessary to develop a decision model that includes both quantitative and qualitative criteria. Considering the decision process as a group decision-making process allows for simultaneous consideration of different perspectives on the decision. Single-valued neutrosophic (SVN) sets are used for expert evaluations. The SVN weighted Dombi Bonferroni mean aggregation operator is employed for criteria weighting. The SVN-alternative ranking using two-step logarithmic normalization (ARLON) method is developed for ranking FVL service providers. The feasibility of the SVN-Dombi Bonferroni-ARLON hybrid algorithm is supported by the case study application. It is concluded that supply chain and logistics managers hold greater importance in FVL service provider selection, with the number of ships being the most critical selection criterion. The robustness of the proposed hybrid method for FVL service provider selection is supported by sensitivity analyses, while its consistency is supported by comparative analyses.

Decision Support System for Permanent Employee Selection at PT XYZ with Moora Method

Using the multi-objective optimization method based on ratio analysis (MOORA), the purpose of this research is to develop a decision support system that will help the selection process of permanent employees at PT XYZ. In order to improve the productivity and operational efficiency of the company, a proper employee selection process is essential. Nonetheless, many factors influence the decision taken, which is often subjective. Therefore, a systematic approach is needed to help managers assess and select potential employees based on predefined standards. The MOORA method was chosen because it is able to provide optimal solutions for a variety of different criteria. The identification of employee selection criteria, the collection of employee candidate data, and the use of the MOORA method to generate employee candidate rankings are all actions performed in this study. The criteria used include work experience, education The purpose of this research is to create a system that supports the process, skills, and attitudes in decision making. The results of this system show that MOORA has the ability to provide more objective and transparent recommendations in the employee selection process than conventional methods. The results of the analysis show that this system helps decision making and increases managers' confidence in choosing employees who match the company's needs. PT XYZ hopes to use this decision support system to reduce errors in the employee selection process and help achieve the company's strategic goals. It is hoped that this research will help the process of developing decision support systems in the field of human resources.

A Prototype Decision Support System for Tree Selection and Plantation with a Focus on Agroforestry and Ecosystem Services

This study presents the development and application of a prototype decision support system (DSS) for tree selection specifically for Punjab, India, a region facing challenges of low forest cover and an increasing demand for sustainable land use practices. The DSS developed using the R Shiny framework integrates ecological, social, and agro-commercial criteria to facilitate scientific knowledge decision making in tree plantation. The modules in this DSS include a tree selection tool based on comprehensive species attributes, a GIS-based tree suitability map module utilizing an Analytical Hierarchical Process (AHP), and a silviculture practice information module sourced from authoritative databases. Combining sophisticated statistical and spatial analysis, such as NMDS and AHP-GIS, this DSS mitigates data redundancy in SDM while incorporating extensive bibliographic research in dataset processing. The study highlights the necessity of fundamental niche-based suitability in comparison to realized niche suitability. It emphasizes on the importance of addressing ecosystem services, agro-commercial aspects, and enhancing silvicultural knowledge. Additionally, the study underscores the significance of local stakeholder engagement in tree selection, particularly involving farmers and other growers, to ensure community involvement and support. The DSS supports agroforestry initiatives and finds applications in urban tree management and governmental programs, emphasizing the use of scientific literature at each step, in contrast to relying solely on local knowledge.

Decision Support System for Selection of Outstanding Students at Angkasa Maros High School Using the TOPSIS Method

Decision Support System for Selection of Outstanding Students at Angkasa Maros High School Using the TOPSIS Method

A picture fuzzy CIMAS-ARTASI model for website performance analysis in human resource management

The central emphasis of human resource management resides in the precise delineation of job responsibilities, engaging in systematic inquiry to identify well-suited candidates, and discerningly electing the most qualified individual from the pool whose qualifications align with the job description. The selection of a well-suited candidate is dependent on the creation of an appropriate candidate pool. Digital platforms are preferred over traditional approaches to reach human resources. The main objective of this research is to identify the most suitable digital platform for posting job advertisements based on website performance. The multiple-attribute group decision-making approach is adopted in this research, considering both qualitative and quantitative criteria. A decision support system for website selection is developed, where expert importance levels are calculated based on picture fuzzy sets (PFS). The weight levels of criteria are determined using the introduced PFS-based criteria importance assessment (CIMAS) method. The ranking of websites is calculated using the proposed PFS-based alternative ranking technique based on adaptive standardized intervals (ARTASI) method. These methods are hybridized into the PFS-CIMAS-ARTASI model. Additionally, an algorithm for this hybrid model is developed. A case study is conducted to demonstrate the applicability of the PFS-CIMAS-ARTASI hybrid model for website performance calculations. Robustness tests based on various sensitivity analysis scenarios are performed. The research results indicate that PFS-CIMAS-ARTASI is both applicable and robust. Comprehensive managerial implications are presented and elaborated on.

Application of Decision Support System for Selection of Residential Criteria using the fuzzy Method in Majene Regency

Application of Decision Support System for Selection of Residential Criteria using the fuzzy Method in Majene Regency

Decision Support System for Selection of An Excellent-Students at Faculty Level in University of X Based on GAP Methods

The aim of this study is to develop a Decision Support System (DSS) for the selection of an excellent-students in one of the faculties of X University using the gap analysis method (GAP). It was used to identify differences between students' expectations of selection criteria and students' actual performance. A survey was conducted on 31 students to obtain the necessary data. The result of this study revealed a gap between students' expectations and performance regarding academic grades and participation in student organizations. The result before implementation GAP with profile matching there were 29 accurate with two inaccurate data (93.54%). After implemented, there were 30 accurate and one was inaccurate at 96.78%. The DSS used GAP is a relevant solution to reduce errors and more objective.

Admission Selection Decision Support System New Students Use the Vikor Method

Admission Selection Decision Support System New Students Use the Vikor Method

Decision Support System For Training Provider Selection Using Profile Matching Method

Decision Support System For Training Provider Selection Using Profile Matching Method

Sistem Informasi PPDB Dengan Menerapkan Metode Simple Additive Weight (SAW) Dalam Sistem Pendukung Keputusan Pemilihan Sekolah

This research proposes and implements a web-based New Student Admission Information System (PPDB) using the Simple Additive Weight (SAW) method as a decision support system for school selection. The case study was conducted at the Lidah Kulon I/464 Surabaya Elementary School. The background of this research is driven by the significant impact of information technology advancements in the education sector. Lidah Kulon I/464 Surabaya Elementary School, as an educational institution committed to achieving excellence, discipline, faith, science and technology (IPTEK), as well as environmental awareness, strives to reduce paper usage in the new student registration process by transitioning to a more efficient administrative information system. The results of the tests state that the system has succeeded in various aspects, including the use of the Certainty Factor Method, diagnosis menu, information menu, login menu, and others. The conclusion of this research is that the system has been implemented successfully, supporting the efficiency of the PPDB process, and providing easy access to users. System development recommendations for the future include flexibility without hard coding, more comprehensive user management, the development of a more advanced school selection system, more accurate entrance fee calculations, and considerations for seasonal factors and school promotions. Keyword: PPDB Information System, Simple Additive Weight (SAW), Method Student Registration, Efficiency Educational Administration

Developed analytic hierarchy process and multi criteria decision support system for wind farm site selection using GIS: A regional-scale application with environmental responsibility

As determined by empirical analysis, renewable energy sources account for less than 1.73 percent of the developing countries energy market. The western region of Iran, or province of Kermanshah, presents significant opportunities for power exports to neighboring country (Iraq). In this research, an appropriate location for wind farms in Western Iran is determined with the assistance of a combined method of Geographic Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) description. Electrical, techno-economic, environmental, and geo-infrastructure aspects, as well as their respective sub-criteria, were subjected to a comprehensive analysis and weighting in this study. The Analytical Hierarchy Process (AHP) was utilized to achieve this result. The aforementioned conclusions were reached through an extensive examination of insightful interviews conducted with esteemed experts in the domain of wind energy. Significant suitability for the potential installation of large-scale wind turbines or wind farms is evident in six critical areas, encompassing more than 20 % of the evaluated region, according to the findings of this paper. Conversely, it was deduced that precisely 80 % of the areas examined were deemed inappropriate for such endeavors. The assessment yielded four locations that were deemed suitable, denoted as A, B, C, and D. The southern, western, and central regions of the province contain these locations. The proposed total capacity is 216 MW, which is determined by the combined surface areas of these regions (19 km2, 55 km2, 64 km2, and 40 km2, respectively). An anticipated reduction of 1,262,471 tons in greenhouse gas emissions is anticipated as a result of the implementation and substitution of the proposed wind farms for the gas turbine power plants that presently predominate in Iran's thermal power plant sector. The method used in this article can be extended to all cases of other faculties to measure the potential of wind farm resources.

Analysis and Development of a Decision Support System for Selection of Prospective KIP Scholarship Recipients

Global Institute of Technology and Business Campus (Global Institute) is one of the campuses that organizes the KIP (Kartu Indonesia Pintar) scholarship program. This program aims to increase access to education for children from disadvantaged families and provide them with financial support. The research methods used are needs analysis, system design, and prototype implementation. This research collects data from various sources, including personal data of prospective scholarship recipients, academic data, family income data, and other relevant factors. The data collected is then analyzed using appropriate decision making methods, such as the Analytical Hierarchy Process (AHP) and TOPSIS methods. A Decision Support System is an interactive platform that helps make decisions efficiently and effectively in the process by presenting alternatives resulting from data processing, information, and the design of interactive computer-based models or systems. This platform provides support to decision makers in utilizing data and models to overcome unstructured problems.

Decision Support System for Selection of Outstanding Students Using the AHP and SAW Methods

It is very important for outstanding students to be directed and guided to get coaching related to the development of each student's personal potential so that superior and quality students are created. The process of selecting outstanding students can get wrong decisions because the process of selecting outstanding students is based on subjectivity, this allows many selected outstanding students not to reach the desired standard and do not get the best candidates. Therefore, a decision support system was created that can carry out the calculation process for all selections for the selection of outstanding students. This final project will implement the AHP and SAW methods in forming a system. The stages are carried out by comparing feature weights with the AHP method. Then the next stage is to rank using the SAW method to get selected outstanding students. Of the 72 students who were selected from the school, they were then selected to become 20 outstanding students based on the highest-ranking order. Software testing is done by comparing the results of school calculations with system calculations. Based on the results of the tests carried out, an accuracy value of 80% was obtained.

Decision Support System For New Employee Selection Using AHP And TOPSIS

There are so many prospective workers with the same educational background, but not necessarily in accordance with the required company position and not necessarily they have the same expertise. To minimize the occurrence of errors, it can be done by making a decision-making system (DSS) to provide these recommendations. In this study, the Analytical Hierarchy Process (AHP) and the Technique For Order Preference by Similarity to Ideal Solution (TOPSIS) method were used to provide recommendations for prospective new employees. The steps taken are to compare the importance of each criterion weight with the AHP method. Then the ranking stage is carried out using the TOPSIS method to get recommendations for selected employees. The data used in this study is primary data in the form of 70 data on prospective employees from PT Hutama Jaya Perkasa. From the 70 data then selected to be 36 prospective employees based on the order of the highest ranking. Software testing is done by comparing the results of system calculations and the results of company calculations. Based on the test results obtained an accuracy value of 94.4%.