Selection Support System Research Articles

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.

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 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%.

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.

Responsible machine learning for United States Air Force pilot candidate selection

The United States Air Force (USAF) continues to be plagued by a chronic pilot shortage, one that could be exacerbated by an accompanying shortfall in the commercial airlines. As a result, efforts have increased to alleviate this shortage by finding methods to reduce pilot training attrition. We contribute to these efforts by setting forth a decision support system (DSS) for pilot candidate selection using modern machine learning techniques. In view of the recent Responsible Artificial Intelligence Strategy published by the United States Department of Defense, this research leverages interpretable and explainable machine learning methods to create traceable and equitable models that may be responsibly and reliably governed. These models are used to regress candidates’ average merit assignment selection system scores based on information available for selection and prior to training. More specifically, using data provided by the USAF from 2010 to 2018, this paper develops and analyzes multiple interpretable models based on Gaussian Bayesian networks, as well as multiple black-box models rendered explainable by SHAP values and conformal prediction. A preferred pair of interpretable and explainable models is selected and embedded within a DSS for USAF pilot candidate selection boards: the Air Force Pilot Applicant Selection System. The utilization of this DSS is explored, the analyses it enables are discussed, and relevant USAF policymaking issues are examined.

Decision Support System for Selection of Internet Services Providers using the ROC and WASPAS Approach

Along with the growth of the internet service provider industry, selecting an Internet Service Provider (ISP) has become an important decision to ensure optimal internet access. However, with so many ISP options available, consumers often face difficulties in choosing the service that best suits their needs. The aim of this research is to produce a decision support system that can help users choose the ISP that best suits their needs and preferences using the ROC (Rank Order Centroid) approach as a weighting technique and the WASPAS (Weighted Aggregated Sum Product Assessment) approach to determine the best alternative. The ROC approach is used to obtain criteria weights based on the ranking order of the importance of the criteria. On the other hand, the WASPAS method is used to determine the best alternative through weighted addition and multiplication, producing a final value that reflects the extent to which each alternative meets the specified criteria. The outcomes of the case study reveal a ranking of alternatives from highest to lowest scores, as follows: First Media (A2) achieving 0.8629, Indihome (A3) at 0.8416, MyRepublic (A5) with 0.7954, Biznet (A1) scoring 0.7844, and Oxygen (A4) at 0.7469. The usability testing yields an average score of 89%, suggesting that the system is apt for utilization, as it aligns with the functionalities users are seeking.

Development of a knowledge base for an expert system supporting the decision of industrial mobile robot selection

With the growth of industrial mobile robots, more and more manufacturing companies are producing such robots. As a result, there is a growing market variety for companies wanting to adopt these robots. It is an economic advantage if a robot selected for a specific task can be operated optimally. To this end, the selection process must already be carried out with optimal operation in mind. In my research, I develop such an industrial mobile robot selection support system, which generates a knowledge base from a database and then supports the selection decision with an expert system. In this paper, I describe the structure, interrelationship and test results of this database and knowledge base.

Decision Support System for Selection of The Best Doctor at Ahmad Brahim Hospital Using the Simple Additive Weighting (SAW) Method

The selection of the best doctors at the Ahmad Brahim hospital was not only done once, but the selection of the best health workers at the Ahmad Brahim hospital was also carried out, but the selection of the best health workers was still done manually by the health department. Therefore, to help speed up the research process, a website-based decision support system was created to select the best doctors to provide rewards with several methods that can be used. One of the methods used in making decisions is simple additive weighting (SAW) which is done by weighting each of the criteria used. Decision support website created using the PHP and MYSQL programming languages for data storage. This study has criterion 3, namely absence, attitude and diligence then have 7 alternatives, the 7 alternatives have 4 ratings 1 where the average assessment is the same as the absence score 0.45, the attitude value is 0.35 and the diligence value is 0.12 with a total value of 1 so those who get the first rank score are dr. Audi Pirade, dr. Kesatria Putra Abadi, dr. Nurfitri Rahmani Awaliyah and dr. Riska Ruswanti. Then the second rank is dr. Christi Angelia Arung Labi with an absence score of 0.45, an attitude value of 0.35 and a diligence value of 0.12 with a total of 0.92

Decision support system for selection of the best teacher at SD Muhammadiyah 18 Medan using the analytical hierarchy process method

Decision support system for selection of the best teacher at SD Muhammadiyah 18 Medan using the analytical hierarchy process method

Sistem Pendukung Keputusan Pemilihan Debt Collector Berprestasi Di PT. Wahana Ottomitra Multiartha Medan Menerapkan Metode SMARTER

An outstanding debt collector must have presence, honesty, teamwork and achievement to improve the debt collector's performance. Problems in selecting outstanding debt collectors at PT. Wahana Ottomitra Multiartha Medan is an election team that finds it difficult to carry out elections quickly and accurately. The decision making technique in the SMARTER (Simple Multi-Attribute Rating Technique Exploiting Ranks) method is based on the theory that each alternative consists of a number of criteria that have values ​​and each criterion has a weight that describes how important it is compared to other criteria. Application of the Simple Multi-Attribute Rating Technique Exploiting Rank method for selecting outstanding debt collectors at PT. Wahana Ottomitra Multiartha Medan provides an accuracy of 23% using the relative standard deviation formula. It is hoped that to overcome this, a Decision Support System for Selection of Outstanding Debt Collectors at PT was built. Wahana Ottomitra Multiartha Medan Implements the SMARTER Method which will be designed using Visual Basic 2008 and using MySQL. In the Visual Basic design, a calculation and ranking process will be produced

Decision Support System for Major Selection in Higher Education for Multimedia Graduate Students using Fuzzy Mamdani Logic

Students at the vocational high school level are indeed prepared to be able to work directly, but it does not rule out the possibility that vocational high school students can continue higher education such as universities. But the problem that will be faced again if students who graduate from vocational high schools choose to continue their education in college is what major they will take. One of the vocational high school majors, namely Multimedia, has a wide scope, so grade 3 vocational high school students who want to go to college have a dilemma in deciding on a major. This research applies the fuzzy logic mamdani to help make decisions for majors in higher education. This study is based on 5 input parameters, namely Komputer dan Jaringan Dasar, Desain Grafis Percetakan, Dasar Desain Grafis, Teknik Audio Visual, and Animasi. There are outputs of 5 majors in Teknik Komputer, Desain Komunikasi Visual, Animasi, TV dan Film, and Fotografi. The results of this research can make it easier and can provide support for grade 3 vocational high school students, especially the Multimedia department in choosing a major in higher education. The results of the decision support system with the highest student score data in Animasi will appear a recommendation score of 79.8 in the Animasi department, and on the highest student score data on the Komputer dan Jaringan Dasar, a recommendation score of 79.8 will appear in the Teknik Komputer major in accordance with the major taken and lived by the current student.

Decision Support System for Student Exchange Selection in Support of Independent Campus using the MAUT and ROC Methods

Independent campus is an off-campus learning activity as an opportunity for students to hone skills in preparation for entering the world of work. One of the programs held is the exchange of students to be able to study on campuses or companies that join the Ministry of Education and Culture, Research and Technology. With the current independent campus program, almost all universities open up opportunities for students to register. However, because there are too many students who register, the campus has difficulty in choosing some students who can be accepted according to the criteria set by the party. For this reason, a Decision Support System is needed that applies the MAUT method along with the use of ROC to produce the best preference value. The calculation of several data samples resulted in the best final utility value in the selection of independent campus student exchanges in Alternative A2 with the best final utility value of 0.9700

Sistem Pendukung Keputusan Pemilihan Sampah Tempat Sementara di Kota Kupang menggunakan Metode Topsis

Temporary waste management in the city of Kupang is an urgent problem to ensure a clean and healthy environment. Selecting the right location for a temporary waste bin is an important step in efficient waste management. In this context, Decision Support Systems (DSS) can provide valuable assistance in the decision-making process. This research proposes a Decision Support System for Selection of Temporary Waste Bins using the TOPSIS Method. The TOPSIS method (Technique for Order of Preference by Similarity to Ideal Solution) is an effective method for overcoming multi-criteria decision-making problems. This system will integrate various factors such as distance to settlements, population density, accessibility, and other environmental factors to evaluate and select optimal temporary waste bins. This decision support system will provide stakeholders, including city governments, waste management authorities, and the community, with tools that can be used to make more informed decisions in determining the location of temporary waste bins. The results of the Decision Support System analysis will provide recommendations based on the level of preference for the specified criteria, enabling stakeholders to make more informed and sustainable decisions in city waste management. It is hoped that this research can help improve efficiency in temporary waste management in Kupang City and has the potential to become a model for other cities that face similar challenges in waste management.