Application Of Decision Support System Research Articles

Recommendation for Prospective Permanent Employees using the Simple Additive Weighting Method

The rapid development of technological progress has made the use of personal computer technology increase significantly, where this use has made computers into branches that can still be developed, one of which is creating a decision-making system. Decision Support System is a computer-based system that is intended to assist decision making by utilizing certain data and models to solve various semi-structured problems. The application of Decision Support Systems can be found in various fields, one of which is a decision support system for prospective employees. This study aims to design a system that can provide the best decision in determining permanent employees at J&T Express Kotanopan. The method used in this study is the SAW (Simple Additive Weighting) method, with a website-based decision support system that can be used without time and place constraints, it can help J&T Express in selecting permanent employees. The results of testing this method have an accuracy level of more than 90% based on the data tested. Based on the results of the highest value obtained using the SAW method, this study was successful in determining permanent employees at J&T Express Kotanopan

Rancangan Sistem Pendukung Keputusan Pemilihan Calon Pegawai Honorer Pemerintah Kabupaten Lamandau Menggunakan Metode Profile Matching

This study aims to design a Decision Support System (DSS) that implements the Profile Matchingmethod for the selection of honorary employees in the Lamandau District Government. The oftennon-systematic selection process results in less objective decisions, necessitating a system that canenhance effectiveness and transparency in decision-making. The Profile Matching method is usedto compare the profiles of candidates with predetermined criteria, leading to a fairer and moreaccurate selection process. Data were collected through interviews with relevant stakeholders inthe Lamandau District Government, which were then analyzed to identify appropriate selectioncriteria. The findings indicate that the application of DSS can reduce subjectivity and improveclarity in the evaluation process. Additionally, the designed computer-based system facilitatesfaster data processing and presents information in a more comprehensible manner. Thus, thisresearch is expected to make a significant contribution to the development of employee selectionsystems in local government and enhance the quality of human resources in public service. Theimplementation of information technology in DSS is anticipated to serve as a reference for futureresearch in decision-making and human resource management.

Application of data-driven Decision Support System in Combating Illegal Wildlife Trade on Internet

With the rapid development of the Internet, the illegal wildlife trade is becoming increasingly serious, posing a threat to biodiversity. Using big data and artificial intelligence technology, this paper identifies abnormal transactions and network relationships by analyzing social media and e-commerce platforms, combined with random forests and graph neural networks, to enhance the efficiency of the crackdown. Random forest algorithms reveal nonlinear patterns and abnormal behavior, and graph neural networks help reveal the complex structure of the transaction network. In implementing the strategy, this paper uses a multi-attribute decision-making model (MADM) and analytic hierarchy Process (AHP) to assess and select the best partners, such as the United Nations Environment Programme, to optimize resource allocation and synergies. Through these technologies, this paper hopes to significantly increase the precision and speed of combating illegal wildlife trade, thereby effectively protecting biodiversity and contributing to the sustainable development of the planet and the harmonious coexistence of the natural environment.

Exploring stakeholder perceptions about using artificial intelligence for the diagnosis of rare and atypical infections.

To evaluate critical care provider perspectives about diagnostic practices for rare and atypical infections and the potential for using artificial intelligence (AI) as a decision-support system (DSS). We conducted an anonymous web-based survey among critical care providers at Mayo Clinic Rochester between 11/25/2023 and 1/15/2024, to evaluate their experience with rare and atypical infection diagnostic processes and AI-based DSSs. We also assessed the perceived usefulness of AI-based DSSs, their potential impact on improving diagnostic practices for rare and atypical infections, and the perceived risks and benefits of their use. A total of 47/143 providers completed the survey. 38/47 agreed that there was a delay in diagnosing rare and atypical infections. Among those who agreed, limited assessment of specific patient factors and failure to consider them were the most frequently cited important contributing factors (33/38). 38/47 reported familiarity with the AI-based DSS applications available to critical care providers. Less than half (18/38) thought AI-based DSSs often provided valuable insights for patient care, but almost three quarters (34/47) thought AI-based DDSs often provided valuable insight when specifically asked about their ability to improve the diagnosis of rare and atypical infections. All respondents rated reliability as important in enhancing the perceived utility of AI-based DSSs (47/47) and almost all rated interpretability and integration into the workflow as important (45/47). The primary concern about implementing an AI-based DSS in this context was alert fatigue (44/47). Most critical care providers perceived that there are delays in diagnosing rare infections, indicating inadequate assessment and consideration of the diagnosis as the major contributors. Reliability, interpretability, workflow integration, and alert fatigue emerged as key factors impacting usability of AI-based DSS. These findings will inform the development and implementation of an AI-based diagnostic algorithm to aid in identifying rare and atypical infections.

Clinical application of clinical decision support systems in the management of patients with atrial fibrillation

Cardiology, like other branches of medicine, is increasingly faced with the need not only to optimize patient treatment, reduce financial costs, but also to improve long-term outcomes. The volume of information required for such tasks is significant, and a doctor’s time is severely limited. Additional software capable of processing large amounts of data in a short period can assist doctors. Clinical decision support system (CDSS) is a type of software that, based on numerous clinical characteristics, provides doctors with information on the most likely diagnosis, patient risk profile, most suitable therapy, and more. Nowadays, CDSS finding more and more applications in cardiology and cardiovascular medicine. The experience of its clinical use has also been accumulated in the Russian Federation. The problem of the correct choice of anticoagulant therapy remains relevant in clinical practice. In addition, despite the proven benefit of prescribing anticoagulants in patients with atrial fibrillation (AF) at a high risk, the frequency of "underprescription" of oral anticoagulants (OACs) remains relatively high. The introduction of a strategy for a personalized approach to the selection of anticoagulants, based on individual patient characteristics, can significantly improve adherence to clinical guidelines and, as a result, reduce the risk of thromboembolic complications. This article discusses the positive and negative aspects of using CDSS in the management of patients with AF, highlighting the main limitations when using them in conditions close to real clinical practice.

The Application of Big Data and Fuzzy Decision Support Systems in the Innovation of Personalized Music Teaching in Universities

Personalized music teaching in universities improves students’ learning and efficiency through adaptive guidance. This adaptability requires large study data and intelligent decisions based on the learner’s ability. This article introduces a Definitive Teaching Support System (DTSS) exclusive to music learning to augment this concept. This system is designed to increase the adaptability of music learning based on student interest and ability. The system is powered by a fuzzy decision system for identifying maximum teaching adaptability to personalized processes. Low-to-high-sorted personalization provides new endorsements for further music sessions in the fuzzy derivative process. Maximum adaptability is the target for new personalized sessions in the universities. This differs for various students from which a common adaptability level for monotonous recommendations is identified. The identified adaptability is set as a global maximum solution towards music learning personalization. The defuzzification reduces the chances of low adaptability by expelling the stationary adaptability outcomes.

Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer.

With the ever-increasing number of artificial intelligence (AI) systems, mitigating risks associated with their use has become one of the most urgent scientific and societal issues. To this end, the European Union passed the EU AI Act, proposing solution strategies that can be summarized under the umbrella term trustworthiness. In anti-cancer drug sensitivity prediction, machine learning (ML) methods are developed for application in medical decision support systems, which require an extraordinary level of trustworthiness. This review offers an overview of the ML landscape of methods for anti-cancer drug sensitivity prediction, including a brief introduction to the four major ML realms (supervised, unsupervised, semi-supervised, and reinforcement learning). In particular, we address the question to what extent trustworthiness-related properties, more specifically, interpretability and reliability, have been incorporated into anti-cancer drug sensitivity prediction methods over the previous decade. In total, we analyzed 36 papers with approaches for anti-cancer drug sensitivity prediction. Our results indicate that the need for reliability has hardly been addressed so far. Interpretability, on the other hand, has often been considered for model development. However, the concept is rather used intuitively, lacking clear definitions. Thus, we propose an easily extensible taxonomy for interpretability, unifying all prevalent connotations explicitly or implicitly used within the field.

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.

Aplikasi Sistem Pendukung Keputusan Menentukan Siswa Terbaik Menggunakan Metode SAW

The research conducted aims to develop a website-based decision support system application that can determine the best students using the Simple Additive Weighting (SAW) Method and developed with the Rapid Application Development (RAD) method. The SAW method was chosen for its simplicity and its ability to accommodate various assessment criteria, including academic grades, attendance, extracurricular participation, and student achievement. The RAD method was used in the development of the system to ensure a rapid development cycle and flexibility in adjusting user needs. The results show that the developed application is able to provide objective and transparent assessment, and simplify the process of determining the best students efficiently. The system also improves accessibility and accuracy in managing assessment data, reduces manual errors, and speeds up decision making. User testing and evaluation showed a high level of satisfaction, with recommendations for additional feature development in the future.

EDAS method for circular pythagorean fuzzy with improved Dombi power aggregation operators and their application in policy analysis and decision support systems

A circular Pythagorean fuzzy set is a very dominant and reliable technique for coping with uncertain and vague information in genuine life problems because it contains the membership function, non-membership function, and radius between both grades with a condition that is the sum of the pair will be contained in the unit interval. The objective of this article is to define some improved Dombi operational laws based on circular improved Pythagorean fuzzy values. Moreover, we discuss some drawbacks of the existing Dombi aggregation operators based on circular improved Pythagorean fuzzy numbers. We also defined some improved Dombi averaging and geometric aggregation operators and their properties. Later on, a technique of evaluation based on distance from the average solution is stated by using the proposed operators. Additionally, we resolve the problem of policy analysis and decision support systems based on multi-attribute decision-making problems for initiated techniques to enhance the worth of the derived theory. Finally, we illustrate some examples for showing the supremacy and validity of the proposed theory with the help of comparative analysis between proposed ranking values and some existing ranking values.

Application of a multi-criteria decision support system for assessing development potential in flood risk areas - Case study of the Warta River

The paper discusses the concept of a proprietary decision-making model that allows for determining whether the planned development in areas not protected by flood embankments and at risk of flooding can be shaped in a safe manner. The model was used to evaluate the possibility of shaping the development in flood-risk areas on the example of one of the types of Olęder villages – a dispersed village located in the floodplains of the Warta River (western Poland). So far, there has been no comprehensive evaluation method supporting proper spatial planning for flood-risk areas. The use of multi-criteria analysis methods enabled to specify the key criteria of this evaluation, which form the basis of the decision-making support system. Additionally, the elaborated method enables to determine whether the localities under study can retain their current functional nature and if there is a potential for further spatial development based on the characteristics of an Olęder village. The presented methodology can be easily adapted to other cultural areas located in countries with different levels of development, traditions, landscape or climate.

Pemanfaatan Metode TOPSIS dalam Membangun Sistem Pendukung Keputusan untuk Menentukan Jenis Penyakit Tanaman Gandum; Studi Kasus di Ladang Gandum Alahan Panjang, Sumatera Barat

Wheat (Triticum aestivum L.) is an annual plant that can grow in highland areas. There are many types of diseases that attack wheat plants, making it difficult for farmers to determine the type of disease. Therefore, it is necessary to build a Decision Support System (DSS) that can determine the type of disease that attacks wheat plants. This research aimed to design and develop a Decision Support System (DSS) application to identify the types of diseases affecting wheat by observing the physical characteristics of the plants. These disease characteristics (criteria) were then inputed into the system, which used an algorithm based on the Technique for Order of Preference by Similarity to a Ideal Solution (TOPSIS) method. The DSS method used was TOPSIS, which considered four criteria: Flower, Stem, Seed, and Leaf. The application development method for the DSS used the Waterfall model, which consisted of the following stages: requirements, definition, system and software design, implementation, and unit testing. A DSS application using the TOPSIS method can determine the type of wheat disease based on physical characteristics as criteria that are inputed into the application. This application can help wheat farmers identify the diseases affecting their plants. The use of the TOPSIS method algorithm in developing the SPK application is an innovation that can help Alahan Panjang wheat farmers in detecting diseases types affecting their crops based on visible physical characteristics. Automatically, the system provides recommendations on the type of disease affecting the wheat plants.

Sistem Pendukung Keputusan Siswa Berprestasi menggunakan Metode Simple Multi Attribute Rating Technique berbasis Web

As one of the private schools in Makassar, Elim Christian High School, the assessment process carried out by teachers on students is still done manually or by recording. Reviewing the assessment of students with academic achievements includes assessments in several subjects as well as non-academic achievements in terms of arts, sports and extracurricular activities. This research aims to design a decision support system application that can assist teachers in managing final grades to determine students with academic and non-academic achievements using a web-based application, using the Simple Multi Attribute Rating Technique Method. The research model included is the SDLC model which starts from analysis, design, implementation, testing and evaluation. Analyze student data and teacher grades. The implementation section creates a web-based application that can carry out the process of selecting the best students. Testing uses black box testing which tests all menus, ensuring the output and functionality are appropriate. The data analysis technique entered is quantitative analysis. Where, quantitative analysis in the form of data is obtained by collecting student data and their grades. The findings are in the form of an application that helps teachers provide recommendations for students who excel both academically and non-academicly in the specified assessment categories. By implementing the Simple Multi Attribute Rating Technique method in implementing the decision support system application, it makes it easier for teachers to determine outstanding students.

Sistem Pendukung Keputusan menggunakan Metode Topsis untuk Seleksi Guru Terbaik

The process of selecting the best teachers is an important aspect of ensuring optimal quality of education in a school. However, the selection process for the best teachers at SMPN 5 Kisaran often faces obstacles such as subjectivity, lack of efficiency, and lack of transparency. This research aims to produce a decision support system using the TOPSIS method to increase objectivity and effectiveness in the teacher selection process, as well as contribute to improving the quality of education. This research is a development type using waterfall, including analysis, design, coding, testing, and implementation stages. The analysis stage is to identify system requirements, the design stage is to determine the system interface, the coding stage is to build the system, and the testing stage is to validate its functionality using a black box. Teacher performance data was collected through interviews, observation, and documentation studies. Teacher performance data will be analyzed using the TOPSIS method, which involves stages of weighting criteria, calculating relative scores, and ranking teachers based on the highest scores. The results of our findings are in the form of a decision support system application for selecting the best teachers. The test results show that this system is running well and by the design and model that has been planned. With this system, it can help SMPN 5 Kisaran in selecting the best teachers.

A simulation-based approach for decision-support in healthcare processes

This article explores the application of simulation-driven decision support systems in the context of healthcare processes. The findings obtained from the review of related research suggest that simulation models have been widely used in the healthcare sector as valuable tools to aid decision-making aimed at the improvement of healthcare process management. However, the analyzed works lack sufficient evidence regarding the utilization of systematic approaches in the development of their proposals. We have conducted a research effort in this field, resulting in the design of a conceptual framework that guides in the systematic development of decision support systems centered around simulation models. The framework includes a specific methodology for developing simulation models, with simulation modeling experimentation serving as a valuable technique for assessing the impact of potential changes on the performance of the process. To illustrate the applicability and practical value of the proposed framework, we have developed an application case within the context of the Urgent Healthcare for Women's (UHW) process in a public hospital in Andalusia, Spain.

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

Logical Platforms for Mobile Application in Decision Support Systems Based on Color Information Processing

Logical Platforms for Mobile Application in Decision Support Systems Based on Color Information Processing

How well do decision support systems help decision makers? An examination of adopting lean manufacturing processes

Choice is crucial for industrial enterprises as their success or failure may depend on it. Consequently, emerging technologies, especially Industry 4.0 (I4.0) are making precision decisions and enabling enriching collaborations with the industry’s computer-assisted decision support systems (DSSs). However, the arrival of Industry 4.0 poses challenges to this emerging application in terms of data variations and interconnectivity. A thorough search across Scopus, Web of Science, and Emerald Science databases yielded 2023 relevant documents. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), the top 250 academic documents were meticulously filtered and analysed in this study. The analysis resulted in the classification of the four main purposes of a DSS as data evaluation, optimisation, scheduling, and selection. The research also investigated the impact of DSSs on the performance of lean manufacturing (LM). Next, this research discussed the controversies with regard to the confidence, prejudice, and discrimination of users, discipline-based DSS application bias, as well as criticisms and suggestions for the future development of DSS, especially in the manufacturing industry. It is believed that, based on its novel findings, this work will pave the way for future research in the same field.

Design and Application of Decision Support System for Educational Management Based on Big Data

Decision support system (DSS) for educational management based on big data leverage advanced analytics and machine learning techniques to enhance decision-making processes within educational institutions. By collecting and analyzing large volumes of data from various sources such as student performance, administrative records, and institutional resources, the DSS provides valuable insights and predictive models to administrators and educators. These insights can inform strategic planning, resource allocation, student interventions, and curriculum development, ultimately improving student outcomes and organizational efficiency. With the ability to process vast amounts of data in real-time, the DSS enables timely and data-driven decision-making, empowering educational leaders to address challenges and capitalize on opportunities in today's complex educational landscape.The integration of big data analytics with the Recommender Ranking Decision Support System (RRDS) presents a transformative approach to enhancing educational management. This paper explores the application of big data analytics within the educational setting, focusing on the development and implementation of the RRDS. By leveraging large and diverse datasets, the RRDS enables personalized learning experiences, optimizes resource allocation, and enhances student outcomes. Through predictive modeling, the RRDS predicts student performance and identifies at-risk students, facilitating targeted interventions to support student success and retention. Additionally, the system offers personalized learning recommendations tailored to individual student needs, fostering engagement and motivation. The findings underscore the significance of data-driven decision-making in improving educational practices and institutional performance. As educational institutions increasingly embrace technology-driven solutions, the integration of big data analytics and RRDS stands poised to revolutionize educational management, ultimately leading to more effective teaching and learning methodologies and better outcomes for students.

PENERAPAN METODE ANALYTICAL HIERARCHY PROCESS PADA SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN JENIS PRODUK SOUVENIR PERNIKAHAN YANG DIMINATI KONSUMEN BERBASIS WEB DI UMKM TAZKIYA SOUVENIR

ABSTRACTTazkiya Souvenir is a wedding souvenir manufacturer that produces various kinds of wedding souvenir products. The purpose of this research is to develop a long process with the help of a decision support system to determine the selection of souvenir products to be purchased, with the results adjusted based on the number of criteria and ranking arrangements from the highest to the lowest. . This study uses the Analytical Hierarchy Process (AHP) method because this method determines the level of importance of each criterion and is followed by a final ranking process. The result of this research is the creation of a decision support system application that can help the process of selecting the type of wedding souvenir product that suits consumer interests based on the highest value ranking results. Based on the calculation process using the Analytical Hierarchy Process method, the first place is Wallet Fold (0.325), the second place is occupied by a Fan (0.304), the third place is a Blacu Bag (0.213) and in the last position is a Pouch (0.156). Tests in this study use functionality testing which is useful for knowing the software can function properly and the system is as expected. Then a validity test was carried out using the McCall method to get a calculation result of 77.8% which is included in the good category. Keywords: Decision Support System, Wedding Souvenir, AHP