Web-based Decision Support System Research Articles

Web-based spatial decision support system for precision agriculture: A tool for delineating dynamic management unit zones (MUZs)

Precision agriculture (PA) is a farming management concept that aims to provide agronomic, economic, and environmental benefits. One of the fields of research in PA is the delineation of Management Unit Zones (MUZs). MUZs are the sub-division of fields featuring an inter-zonal variation delineated by agronomists for on-field PA operations. To develop MUZs, three factors typically need to be considered: input multi-dimensional data, procedures to process the information, and the optimal number of zones a field should be divided into. PA uses digital technologies to collect and analyze a large amount of data, outline MUZs, monitor crops, and carry out site-specific crop management. Web-based spatial decision support systems (WB-SDSS) can provide users with tools that ease the complex procedures for PA. The objective of this study is twofold: on the one hand, we developed a free and open source (FOSS) WB-SDSS to facilitate the implementation and use of such tools for delineating MUZs and monitoring crops; on the other, a MUZs outline procedure was developed based on Sentinel-2 and Planetscope time series data, and spatio-temporal dynamic clustering model using fuzzy c-means. Our study highlighted that the WB-SDSS might be a helpful solution for harmonizing data collected from different sources, easing the implementation and use of complex geospatial procedures for PA, and delineating MUZs. We tested the system on a particularly representative farm in the Emilia Romagna region (Northern Italy), with 512 hectares of durum wheat crops. Using the WB-SDSS, we quickly delineated homogeneous zones for 27 fields in the study area during the phenological cycle of durum wheat (November 2018-June 2019).

Design of a Web-Based Decision Support Information System for YMM FI Scholarship Recipients Using the SMART Method

Designing a Web-Based YMM FI Scholarship Recipient Decision Support Information System Using the SMART Method can help YMM FI in the decision-making process to determine the right scholarship recipient. YMM FI is a zakat fund management foundation which has several programs, one of which is still running, the Edu Care Program which focuses on funding education from the lower to upper levels. However, the selection of prospective recipients of higher education scholarships is still done manually, which results in inaccuracies in those who receive them. For this reason, there is a need for a Web-Based Decision Support System using the SMART Method which takes into account several attributes that have weighted values and criteria. From the results found, attribute A2 with the name Wa Marlin is the highest ranking of the other attributes

D3S: Decision support system for sectorization

Sectorization problems refer to dividing a large set, area or network into smaller parts concerning one or more objectives. A decision support system (DSS) is a relevant tool for solving these problems, improving optimisation procedures, and finding feasible solutions more efficiently. This paper presents a new web-based Decision Support System for Sectorization (D3S). D3S is designed to solve sectorization problems in various areas, such as school and health districting,planning sales territories and maintenance operations zones, or political districting. Due to its generic design, D3S bridges the gap between sectorization problems and a state-of-the-art decision support tool. The paper aims to present the generic and technical attributes of D3S by providing detailed information regarding the problem-solution approach (based on Evolutionary Algorithms), objectives (most common in sectorization), constraints, structure and performance.

Nurses' use of an advisory decision support system in ambulance services: A qualitative study.

To illuminate from the perspective of nurses in ambulance services the experiences of using a web-based advisory decision support system to assess care needs and refer patients. Inductive and descriptive approaches. Thirteen semi-structured interviews were conducted in the spring of 2020. The data were analysed through the reflexive thematic analysis. The Swedish web-based advisory decision support system (ADSS) was found to strengthen nurses' feelings of security when they assess patients' care needs, promote their competence and professional pride, and help them manage stress. However, the system also generated difficulties for nurses to adjust to the dynamic ambulance team and revealed a discrepancy between their professional roles and responsibilities to refer patients and provide self-care advice. The nurses thought that the support system facilitated their increased participation and helped them understand patients and significant others by offering transparency in assessment and decision making. Thus, the support system provides nurses with an opportunity to strengthen patients' independence through information and education. However, in the care relationship, nurses worked to overcome patients' expectations. Nurses using the ADSS increased their security while performing assessments and referrals and found new opportunities to provide information and promote understanding of their decisions. However, nursing care values can be threatened when new support systems are introduced, especially as ambulance services become increasingly protocol-driven. These findings have implications for nurses' work environments and help them maintain consistency in making medical assessments and in providing equivalent self-care advice when referring patients to the different levels of care. The findings will also impact researchers and policymakers who formulate decision support systems. Consolidated criteria for reporting qualitative research (COREQ). None.

A web-based interactive decision support system for a multi-objective lot-sizing and production scheduling model

In the course of globalization and the corresponding steadily growing market dynamics, companies are faced with an increasing challenge to remain competitive. Therefore, the efficient use of the available resources, while maintaining a high level of service and customer satisfaction, is a key instrument for ensuring success. Within this framework, production scheduling can be seen as one of the most decisive operational activities of a manufacturing company, with the production orders being sequenced in a way that allows the achievement of an efficient allocation of the available resources. Furthermore, the correlated lot-sizing represents a crucial influencing factor in production scheduling. In this article, a novel methodology to solve multi-objective lot-sizing and production scheduling problems is proposed. Essentially, it provides an interactive three-phase framework that supports a procedure for simultaneous decision-making regarding lot-sizing and production scheduling. The main goal is not only to find an appropriate production schedule that best meets the decision-maker’s preferences but also to gain a deeper insight into the relationship among criteria and a better understanding of possible trade-offs between competing objectives. To guide the user through the whole decision-making process, a web-based interactive application was developed. The proposed methodology was applied to a case study regarding an automotive supplier company, revealing itself as a viable and practical technique able to solve real-world multi-objective lot-sizing and production scheduling problems in the manufacturing industry.

Systematic Review of Web-Based Decision Support Systems for Clinical Applications: Enhancing Ontology with Unified Modeling Language and Ontology Web Language

This article demonstrates an ontology-based technique that aids in envisioning the problem domain before software development, presenting an approach to assist decision-making for reliable and optimal healthcare administration. The proposed approach is designed for utilization by a Web-Based Decision Support System (WebDSS). The purpose of the study is to aid readers in comprehending how ontologies in healthcare management platforms can be modeled using UML and OWL. For the conceptualization and the design, service modelling and the semantics of the web language are crucial. We demonstrate the use of the Unified Modeling Language (UML) and the Ontology Web Language (OWL) in the administration of healthcare operations and key elements of the model-base of healthcare administration systems. Highly educated systems with automated assistance will allow decision-makers to more effectively incorporate aspects of sustainability into their decisions. In order to generate semantic ontologies for medical management, the present study proposes an ontology creation technique and a Semantic Web framework called Protégé. Web-based technologies, decision trees, data mining, and computational algorithms are implemented by WebDSS to assess medical resources administration information in real-time. These methods are employed to develop a decision making framework for this study. Time series graphs, email reports, and webpages are used to display the test findings. When healthcare thresholds are surpassed, the system uses text messages as email reports to automatically deliver warnings. Our study's primary contribution is the creation of a useful tool that makes it easier for academics studying medical concerns to locate online sites with information.

Multi-Modelling Approach to Support Decision Making in Crop Protection

Abstract A more sustainable control of plant diseases is needed for the development of less intensive farming systems with reduced inputs of chemicals, including pesticides, and reduced use of natural resources. Integrated pest management (IPM) is currently the most effective method for sustainable disease control. IPM has higher complexity than conventional disease management and involves decision making at strategic, tactic, and operational levels, which is often challenging for farmers. Mathematical models are simplified representations of a crop system, which provide dynamic information about the crop status and can support decision making. In an IPM approach, plant disease models must be complemented with other models, such as models for crop growth and development, and models for fungicide dynamics. This case study describes the integration of mathematical models for plant disease, crop development, and plant protection products in a multi-modelling approach able to support multiple decision making in IPM. Downy mildew ( Plasmopara viticola) of grapevines ( Vitis ) is used as a successful story of multiple model combinations through a web-based decision support system. Information © The Author 2024

Decision support system in determining the location of new supermarket branches using the copras method

Supermarkets are one of the ideal and profitable retail business sectors to try because they are located in various urban and rural areas. This causes many people to be interested in setting up a supermarket. However, determining a strategic location is not easy and requires many strategic location considerations. The research objective is to develop a Decision Support System (DSS) to determine the location of new supermarket branches using the Complex Proportional Assessment (COPRAS) method, which is expected to be helpful for management and supermarket partners as a business strategy. The COPRAS method excels in calculating alternative utilities and selecting the best alternative. There are nine criteria (land rental price, distance to competitors, security, distance to education, warehouse distance, cleanliness, land area, building price, crowd) and five alternative locations (Juanda, Hos Cokroaminoto, Bayangkara, Batoro Katong, Sumoroto) are considered. This research created a web-based DSS that selects the best location for supermarket, with Juanda (A1) ranked first and scored 100, followed by Somoroto (location A5) with a score of 99.861, Bayangkara (A3) with a score of 97.099, Batoro Katong (A4) with a score of 91.293, and HOS Cokroaminoto (A2) with a score of 88.877. From the results of the COPRAS calculation, it can be concluded that Juanda is the best location to build a new supermarket branch location. This result provides a valuable tool for management and supermarket partners seeking to make informed decisions about branch expansion strategies.

Sistem Pendukung Keputusan dalam Menentukan Strategi Manajemen Resiko UMKM Berbasis Web Dengan Metode Certainty Factor

Risk management plays a crucial role in securing the sustainability and growth of Micro, Small, and Medium Enterprises (MSMEs). Amidst the ever-changing complexity of the business environment, an efficient approach is needed to identify, analyze, and address the risks faced by MSMEs. This research aims to investigate the implementation of web-based Decision Support Systems (DSS) using the Certainty Factor method as a risk management strategy for MSMEs. The risk data obtained will be used to build a knowledge base in the form of a decision support system integrated into a web application. The implementation of web-based DSS provides wide accessibility advantages for MSMEs in managing risks. By utilizing the web application, MSME stakeholders can identify relevant risks in their field of business, analyze the impact and probability of each risk, and receive appropriate action recommendations. The results of this research indicate that the implementation of web-based DSS using the Certainty Factor method is effective in supporting MSMEs in risk management. MSMEs that utilize this application can make more informed and timely risk decisions, reducing potential losses. Therefore, it is hoped that this research can make a significant contribution to enhancing the resilience of MSMEs in facing the complexities of the business environment.

Penerapan Metode Weighted Product Pada Sistem Pendukung Keputusan Pemilihan Calon Dosen Pembimbing Skripsi

Decision Support System is a computer system that processes data into information to make decisions regarding specific semi-structured problems. It is specially developed to support solutions for unstructured management issues to enhance the quality of decision-making. The selection of potential thesis advisors at Hamzanwadi University's Informatics Engineering program is currently done by directly choosing an advisor while considering individual criteria or by seeking advice from peers. However, this direct selection method sometimes overlooks crucial aspects that should be prioritized. This research aims to construct a web-based Decision Support System (DSS) to assist final-semester students in the Faculty of Engineering at Hamzanwadi University in selecting their thesis advisors. The methodology employed in this Decision Support System is the Weighted Product method, which enables the calculation and comparison of multiple factors during the decision-making process. Factors analyzed in this study encompass the instructors' competency areas and the number of students they supervise. The utilization of this Decision Support System is expected to aid students in choosing an appropriate and fitting thesis advisor according to their needs. Moreover, the Decision Support System is anticipated to aid the faculty in providing information about potential thesis advisors before students finalize their choices. This research is expected to contribute to and benefit the development of information technology in the education sector

A Machine Learning Model to Predict Patients' Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial.

Adherence to oral anticancer treatments is critical in the disease trajectory of patients with breast cancer. Given the impact of nonadherence on clinical outcomes and the associated economic burden for the health care system, finding ways to increase treatment adherence is particularly relevant. The primary end point is to evaluate the effectiveness of a decision support system (DSS) and a machine learning web application in promoting adherence to oral anticancer treatments among patients with metastatic breast cancer. The secondary end point is to collect a set of new physical, psychological, social, behavioral, and quality of life predictive variables that could be used to refine the preliminary version of the machine learning model to predict patients' adherence behavior. This prospective, randomized controlled study is nested in a large-scale international project named "Enhancing therapy adherence among metastatic breast cancer patients" (Pfizer 65080791), aimed to develop a predictive model of nonadherence and associated DSS and guidelines to foster patients' engagement and therapy adherence. A web-based DSS named TREAT (treatment adherence support) was developed using a patient-driven approach, with 4 sections, that is, Section A: Metastatic Breast Cancer; Section B: Adherence to Cancer Therapies; Section C: Promoting Adherence; and Section D: My Adherence Diary. Moreover, a machine learning-based web application was developed to predict patients' risk factors of adherence to anticancer treatment, specifically pertaining to physical status and comorbid conditions, as well as short and long-term side effects. Overall, 100 patients consecutively admitted at the European Institute of Oncology (IEO) at the Division of Medical Senology will be enrolled; 50 patients with metastatic breast cancer will be exposed to the DSS and machine learning web application for 3 months (experimental group), and 50 patients will not be exposed to the intervention (control group). Each participant will fill a weekly medication diary and a set of standardized self-reports evaluating psychological and quality of life variables (Adherence Attitude Inventory, Beck Depression Inventory-II, Brief Pain Inventory, 13-item Sense of Coherence scale, Brief Italian version of Cancer Behavior Inventory, European Organization for Research and Treatment of Cancer Quality of Life 23-item Breast Cancer-specific Questionnaire, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, 8-item Morisky Medication Adherence Scale, State-Trait Anxiety Inventory forms I and II, Big Five Inventory, and visual analogue scales evaluating risk perception). The 3 assessment time points are T0 (baseline), T1 (1 month), T2 (2 months), and T3 (3 months). This study was approved by the IEO ethics committee (R1786/22-IEO 1907). The recruitment process started in May 2023 and is expected to conclude on December 2023. The contribution of machine learning techniques through risk-predictive models integrated into DSS will enable medication adherence by patients with cancer. ClinicalTrials.gov NCT06161181; https://clinicaltrials.gov/study/NCT06161181. DERR1-10.2196/48852.

PROBLEMS OF THE RECLAMATION AND WATER MANAGEMENT COMPLEX THE LOWER DON IN THE CURRENT CLIMATIC CONDITIONS

Long-term climatic changes have led to a decrease in the water content of a number of Russian rivers. In the pool of R. The Don phase of low water content has been going on since 2007. In addition, due to climate warming, significant fluctuations in the intra-annual hydrological regime are observed. The climatic situation and high anthropogenic pressure on water resources complicate the conditions for the development of irrigated agriculture. At the same time, there is an obvious need to increase the area of irrigated land on the Lower Don. The authors of the article propose a number of measures that contribute to the effective development of the Nizhny Don water management complex against the background of water scarcity. The possibility of replenishing the flow of the Don River is considered. In the conditions of the Azov-Don basin, a geoinformation web-based decision support system for integrated management of the reclamation and water management complex developed at the Federal Scientific Center for Hydraulic Engineering and Melioration named after A.N. Kostyakov can be used. The proposed hydrodynamic model of the Lower Don can be used in order to form optimal modes of operation of the Tsimlyansk hydroelectric complex in the interests of agricultural and fisheries water users.

A Web-Based Decision Support System for Project Evaluation with Sustainable Development Considerations Based on Two Developed Pythagorean Fuzzy Decision Methods

Decision support systems are being developed as attractive tools to help organizations make better decisions. These systems assist decision-makers in making the best decisions. The widespread application of the internet has transformed the development of decision support systems into a web-based challenge. On the other hand, project selection has always been a significant issue for organizations. The limitation of resources and the existence of different criteria while selecting projects cause organizations to face the challenges of multiple-criteria decision making. In this research, a new approach is introduced for the selection of criteria. It also presents a new web-based decision support system for selecting projects considering uncertainty and various criteria, including organizational strategies, the seventh edition of project management standard, and sustainable development. Therefore, the economic, social, and environmental dimensions of sustainable development were included as project evaluation indicators. The proposed approach was developed using Pythagorean fuzzy sets, MEREC, and MARCOS methods to examine uncertainty and solution methods. In this approach, a new version of the MARCOS method was developed, with Pythagorean fuzzy sets for rankings. Also, a new development was presented using the Pythagorean fuzzy (PF)-MEREC method, which was used for weighting. The effectiveness of the proposed method is discussed through a real case study conducted on one of the mineral holdings in Iran. Among the mining projects introduced to the company, finally, the second project was selected. In the comparison made using PF-Entropy-TOPSIS and PF-Entropy-VIKOR methods, the superior project provided similar results. By changing the weights of the criteria for four different types of states, sensitivity analysis was used to determine the reliability of the final rankings. In these states, the weights of the criteria were moved together or assigned equal weights, and, in all four states, the ranking results were the same.

Diagnostic clinical decision support based on deep learning and knowledge-based systems for psoriasis: From diagnosis to treatment options

Psoriasis is an acute immuno-dermatological disease, affecting people of all ages, which significantly decreases quality of life. While the standard approach to identification and diagnosis of psoriasis is based on dermatologist decisions, various Deep Learning (DL) methods have been utilized to create Computer-Aided Diagnosis (CAD) systems to detect and classify psoriasis cases. In response to the knowledge gap of an existing practical and functional DL-based solution to psoriasis diagnosis, this study proposed an ensemble Convolutional Neural Network (CNN) model using Residual Network 50 Version 2 (ResNet50V2), ResNet101V2, and ResNet152V2 networks to create a CAD system for detecting and classifying psoriatic images. This ensemble model determines whether an input image is psoriatic using a binary classification procedure in the initial stage and classifies the psoriatic images into seven variants utilizing a multi-class classification. Furthermore, a treatment suggestion system was embedded within the diagnostic algorithm to suggest the best treatment options for psoriasis variants using a Multi-Criteria Decision Making (MCDM) method with the aim of reducing the disease symptoms in patients. A web-based Decision and Diagnostic Support System (D&DSS) is constructed to determine whether an input image is psoriatic, classify the psoriatic images into different variants, and accordingly recommend the best treatment options based on the observed disease symptoms in a patient. Nevertheless, the functionality and reliability of the proposed D&DSS are validated with high accuracy rates in both diagnostic and identification stages of the approach, which ratifies the practicality of this proposition.

IMPLEMENTATION OF SIMPLE ADDITIVE WEIGHTING (SAW) METHOD FOR DESIGN AND BUILD DECISION SUPPORT SYSTEM OF LAPTOP SELECTION (Case Study: IT Store)

Laptop is a small computer that is easy to carry anywhere, which makes everyone want to have one because of its convenience. However, the laptops currently available have various specifications, so users feel confused about choosing and buying the laptop they want. Therefore, this research was conducted to build a web-based decision-support system. The method used in this decision support system is simple additive weighting as a criterion for calculating laptop recommendations. The SAW method is a weighted addition method to determine the weighted sum to determine ranking. User satisfaction test results using a questionnaire using the End User Computing Satisfaction (EUCS) method The results of the calculation of the user satisfaction test obtained a result of 85.66%, which means that the system built can be useful for users in providing laptop recommendations.

MULTI-CRITERIA DECISION ANALYSIS USING COMPLEX PROPORTIONAL ASSESSMENTS AND RANK ORDER CENTROID METHODS IN THE SELECTION SYSTEM FOR TUTORING INSTITUTIONS

Tutoring can help increase students' self-confidence, reduce anxiety about tests or assignments, and overcome learning barriers. The large number of tutoring institutions that offer various programs makes parents or students have to be observant when choosing them. To choose a tutoring institution, parents or students must know all the profiles and programs of the institution to be selected. This creates a difficult and long time to come up with a choice. The purpose of this study is to use the Multi Criteria Decision Analysis (MCDA) approach through the Complex Proportional Assessment (COPRAS) method and the Rank Order Centroid (ROC) method to create a Decision Support System (DSS) that will make it easier to choose a tutoring institution. The ROC approach serves to determine the weight based on the order of importance of the criteria. The COPRAS method is used because this method takes utility into account by assessing the usefulness of each alternative. This research produced a web-based tutoring institution selection DSS that can provide alternative recommendations based on criteria determined by decision-makers. The results of system calculations and manual calculations do not show a different value, which shows that the system produces a valid COPRAS approach value. Based on the results of usability testing, the built DSS scored 89.17%; in other words, the system is feasible to use.

Klasifikasi Tingkat Kerusakan Sektor Pasca Bencana Alam Menggunakan Metode MULTIMOORA Berbasis Web

During 2020-2021, 10,152 disasters occurred in Indonesia, significantly impacting the affected sectors. The recovery of these sectors needs to be done as quickly as possible to maintain human survival. This study aims to analyze the factors that affect sector damage after natural disasters in Indonesia and measure the classification accuracy. The data used in this research is data from the Regional Disaster Management Agency of Malang City in 2020. This study developed a web-based Decision Support System (DSS) using The Multiplicative Form Integrated MOORA (MULTIMOORA) method. This method is the result of the development of the MOORA method by adding a complete multiplication form to the MOORA method. In this study, the MULTIMOORA method was used to classify the level of damage to sectors after natural disasters. The results showed that using the MULTIMOORA method in this DSS resulted in an accuracy rate of 84% and was included in the good enough category.

Sistem Pendukung Keputusan Pemilihan SMA Berbasis Web Menggunakan Metode Analytical Hierarchy Process (Ahp) dan Topsis

The advancement of science and technology has brought about changes in various aspects of human life, including the selection of secondary schools. Effective decision-making requires the application of the best method from various available solution alternatives. Computers have proven their role in enhancing efficiency and positive impact in various human activities. This research aims to address the complexities of selecting a high school by developing a web-based Decision Support System (DSS) using the Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. This thesis encompasses user requirement analysis, system design and implementation, as well as result evaluation. The outcome of this study is a system that assists 9th-grade students of SMP Kawasan Industri Ciampel (KIC) in choosing a suitable high school based on their preferences and criteria. In the system implementation, the AHP method is used to calculate the weights of different criteria, and the TOPSIS method is utilized to rank schools based on user-inputted preferences. This system provides more accurate and focused school recommendations, simplifying the decision-making process for students and parents. Further development could extend the geographical scope and criteria for school selection, as well as enhance the efficiency of the AHP and TOPSIS algorithms. Real-time data integration could also provide more up-to-date information to users. The conclusion of this research is that this web-based decision support system can aid students and parents in selecting a suitable high school, with potential for further development to offer greater benefits.

Web-based eHealth Clinical Decision Support System as a tool for the treat-to-target management of patients with systemic lupus erythematosus: development and initial usability evaluation

BackgroundTreat-to-target (T2T) is a therapeutic strategy currently being studied for its application in systemic lupus erythematosus (SLE). Patients and rheumatologists have little support in making the best treatment decision in...

SISTEM PENDUKUNG KEPUTUSAN CALON PENERIMA DANA BANTUAN SISWA MISKIN (BSM) MENGGUNAKAN METODE MULTI-OBJECTIVE OPTIMAZION ON THE BASIS OF RATIO ANALYSIS

<p><strong>Abstract :</strong><em>.</em> <em>Poor student assistance funds are assistance given to students from underprivileged families to be able to carry out learning activities at school. This assistance provides opportunities for students to pursue higher-level education.</em></p><p><em>This program aims to enable students from underprivileged groups to continue their education at school. In addition, it also aims to reduce the number of students dropping out of school due to problems with education costs.</em></p><p><em> The system used by the school still uses Microsoft Excel. In previous research, there was a system that implemented several methods to help the SMK UNTUNG SURAPATI school in determining the receipt of aid funds for poor students. To assist the SMK UNTUNG SURAPATI in selecting the recipients of funds for poor students, a web-based decision support system was created. The method used is the moora method (Multi – Objective Optimization On The Basis Of Ratio Analysis). The moora method is implemented on the system to be built. The application of the moora method requires a weighting of each criterion.</em></p><p><em>With the MOORA method, reports on aid funds for poor students that were previously standard and unstructured have turned into structured and directed so that the SMK UNTUNG SURAPATI schools that use this application can get a view to select students who are entitled to the assistance based on the highest score results.</em></p>