Implementation Of Decision Support System Research Articles

Design and Implementation of an Intelligent Metro Project Investment Decision Support System

The construction of subway projects involves tight engineering cycles, multiple technical challenges, and complex coordination among various stakeholders. Due to the influence of uncertain factors during the construction process, the investment in subway project construction exhibits non-linear changes over time. Investment decision-making is the process through which the investment entity determines its investment activities. For typical investment entities, project investment decision-making primarily entails analyzing and evaluating proposed engineering projects based on investigation, analysis, and argumentation, ultimately deciding whether to invest. With the widespread application of information technology (IT) across various fields, decision support systems (DSS) have emerged to enhance the decision-making capabilities of enterprise management. This article designs an intelligent subway project investment DSS, leveraging data mining (DM) technology to integrate DSS with a data warehouse (DW).

Implementation of Agile Methods in GIS Web Applications for Land Suitability Selection

Perusahaan Daerah Perkebunan (PDP) Kahyangan is a Regional Owned Enterprise (BUMD) in Jember Regency which was formed in order to manage plantation commodities in 3 Main Plantations and two Subdivision Plantations. PDP’s profit contribution to Jember’s PAD (Pendapatan Asli Daerah) has continued to decline in recent years. With a target of PAD from plantation products in 2020 of 20 million rupiah, only 20% can be realized (4 million rupiah). Based on the 2023 PDP Kahyangan Jember Strategic Plan, one of the problems causing the opinion deficit is external factors. One of them is the suitability between the potential of the plants planted and the land conditions in a plantation area. Where land suitability, which is the carrying capacity of the land, has a significant effect on crop production. Based on the problems that have been presented, it is necessary to design a GIS-based Decision Support System (DSS), which can provide insight to researchers, practitioners and policy makers regarding the success of managing GIS data for the land suitability evaluation process so that it can increase agricultural productivity. Several stages that will be carried out include data collection, data preparation, and data processing carried out in GIS, then continued with implementation of Decision Support System. The used of agile methods to the application development process ensures that the application is built in a shorter time through the use of continuous iteration and testing thereby ensuring that the DSS implementation on the Web GIS application can run optimally.

AI can empower agriculture for global food security: challenges and prospects in developing nations.

Food and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030-Goal 2 of the United Nations Sustainable Development Goals (UNSDG). This goal cannot be achieved unless the digital divide among developed and underdeveloped countries is addressed. Nevertheless, developing and underdeveloped regions fall behind in economic resources; however, they harbor untapped potential to effectively address the impending demands posed by the soaring world population. Therefore, this study explores the in-depth potential of AI in the agriculture sector for developing and under-developed countries. Similarly, it aims to emphasize the proven efficiency and spin-off applications of AI in the advancement of agriculture. Currently, AI is being utilized in various spheres of agriculture, including but not limited to crop surveillance, irrigation management, disease identification, fertilization practices, task automation, image manipulation, data processing, yield forecasting, supply chain optimization, implementation of decision support system (DSS), weed control, and the enhancement of resource utilization. Whereas AI supports food safety and security by ensuring higher crop yields that are acquired by harnessing the potential of multi-temporal remote sensing (RS) techniques to accurately discern diverse crop phenotypes, monitor land cover dynamics, assess variations in soil organic matter, predict soil moisture levels, conduct plant biomass modeling, and enable comprehensive crop monitoring. The present study identifies various challenges, including financial, infrastructure, experts, data availability, customization, regulatory framework, cultural norms and attitudes, access to market, and interdisciplinary collaboration, in the adoption of AI for developing nations with their subsequent remedies. The identification of challenges and opportunities in the implementation of AI could ignite further research and actions in these regions; thereby supporting sustainable development.

Decision support systems for antibiotic prescription in hospitals: a survey with hospital managers on factors for implementation.

Inappropriate antimicrobial use, such as antibiotic intake in viral infections, incorrect dosing and incorrect dosing cycles, has been shown to be an important determinant of the emergence of antimicrobial resistance. Artificial intelligence-based decision support systems represent a potential solution for improving antimicrobial prescribing and containing antimicrobial resistance by supporting clinical decision-making thus optimizing antibiotic use and improving patient outcomes. The aim of this research was to examine implementation factors of artificial intelligence-based decision support systems for antibiotic prescription in hospitals from the perspective of the hospital managers, who have decision-making authority for the organization. An online survey was conducted between December 2022 and May 2023 with managers of German hospitals on factors for decision support system implementation. Survey responses were analyzed from 118 respondents through descriptive statistics. Survey participants reported openness towards the use of artificial intelligence-based decision support systems for antibiotic prescription in hospitals but little self-perceived knowledge in this field. Artificial intelligence-based decision support systems appear to be a promising opportunity to improve quality of care and increase treatment safety. Along with the Human-Organization-Technology-fit model attitudes were presented. In particular, user-friendliness of the system and compatibility with existing technical structures are considered to be important for implementation. The uptake of decision support systems also depends on the ability of an organization to create a facilitating environment that helps to address the lack of user knowledge as well as trust in and skepticism towards these systems. This includes the training of user groups and support of the management level. Besides, it has been assessed to be important that potential users are open towards change and perceive an added value of the use of artificial intelligence-based decision support systems. The survey has revealed the perspective of hospital managers on different factors that may help to address implementation challenges for artificial intelligence-based decision support systems in antibiotic prescribing. By combining factors of user perceptions about the systems´ perceived benefits with external factors of system design requirements and contextual conditions, the findings highlight the need for a holistic implementation framework of artificial intelligence-based decision support systems.

Improvements in appropriate placement of dental sealants after implementation of a clinical decision support system

BackgroundDental sealants are effective for the prevention of caries in children at elevated risk levels, and increasing the proportion of children and adolescents who have dental sealants on 1 or more molars is a Healthy People 2030 objective. Electronic health record (EHR)-based clinical decision support systems (CDSSs) have the ability to improve patient care. A dental quality measure related to dental sealant placement for children at elevated risk of caries was targeted for improvement using a CDSS. MethodsA validated dental quality measure was adapted to assess a patient’s need for dental sealant placement. A CDSS was implemented to advise care team members whether a child was at elevated risk of developing caries and had sealant-eligible first or second molars. Data on dental sealant placement at examination visits during a 5-year period were analyzed, including 32 months before CDSS implementation and 28 months after CDSS implementation. ResultsFrom January 1, 2018, through December 31, 2022, the authors assessed 59,047 examination visits for children at elevated risk of developing caries and with sealant-eligible teeth. With the implementation of a CDSS and training to support the clinical care team members in September 2020, the appropriate placement of dental sealants at examination visits increased from 27% through 60% (P < .00001). ConclusionsIntegration of a CDSS into the EHR as part of a quality improvement program was effective in increasing the delivery of sealants in eligible first and second molars of children aged 5 through 15 years and considered at high risk of developing caries. Practical ImplicationsAn EHR–based CDSS can be implemented to improve standardization and provide timely and appropriate patient care in dental practices.

Implementation of an Electronic Clinical Decision Support System for the Early Recognition and Management of Dysglycemia in an Inpatient Mental Health Setting Using CogStack: Protocol for a Pilot Hybrid Type 3 Effectiveness-Implementation Randomized Controlled Cluster Trial.

Severe mental illnesses (SMIs), including schizophrenia, bipolar affective disorder, and major depressive disorder, are associated with an increased risk of physical health comorbidities and premature mortality from conditions including cardiovascular disease and diabetes. Digital technologies such as electronic clinical decision support systems (eCDSSs) could play a crucial role in improving the clinician-led management of conditions such as dysglycemia (deranged blood sugar levels) and associated conditions such as diabetes in people with a diagnosis of SMI in mental health settings. We have developed a real-time eCDSS using CogStack, an information retrieval and extraction platform, to automatically alert clinicians with National Health Service Trust-approved, guideline-based recommendations for dysglycemia monitoring and management in secondary mental health care. This novel system aims to improve the management of dysglycemia and associated conditions, such as diabetes, in SMI. This protocol describes a pilot study to explore the acceptability, feasibility, and evaluation of its implementation in a mental health inpatient setting. This will be a pilot hybrid type 3 effectiveness-implementation randomized controlled cluster trial in inpatient mental health wards. A ward will be the unit of recruitment, where it will be randomly allocated to receive either access to the eCDSS plus usual care or usual care alone over a 4-month period. We will measure implementation outcomes, including the feasibility and acceptability of the eCDSS to clinicians, as primary outcomes, alongside secondary outcomes relating to the process of care measures such as dysglycemia screening rates. An evaluation of other implementation outcomes relating to the eCDSS will be conducted, identifying facilitators and barriers based on established implementation science frameworks. Enrollment of wards began in April 2022, after which clinical staff were recruited to take part in surveys and interviews. The intervention period of the trial began in February 2023, and subsequent data collection was completed in August 2023. Data are currently being analyzed, and results are expected to be available in June 2024. An eCDSS can have the potential to improve clinician-led management of dysglycemia in inpatient mental health settings. If found to be feasible and acceptable, then, in combination with the results of the implementation evaluation, the system can be refined and improved to support future successful implementation. A larger and more definitive effectiveness trial should then be conducted to assess its impact on clinical outcomes and to inform scalability and application to other conditions in wider mental health care settings. ClinicalTrials.gov NCT04792268; https://clinicaltrials.gov/study/NCT04792268. DERR1-10.2196/49548.

A decision support system for effective implementation of agro-environmental measures targeted at small woody landscape features: The case study of Slovenia

Years of agricultural intensification across Europe’s plains, valleys and hills had left behind a more or less generalized land use pattern dominated by profitable land cover types. Small landscape features with no direct (financial) benefit to the farmer, have been, in many cases, removed and converted to arable land. This study investigates the possibilities to integrate small woody landscape features (SWLF) into the new (2023–2027) CAP measures, which are in conjunction with the EU Green Deal ambitions and support biodiversity preservation in agroecosystems. A spatial SWLF preservation/restoration algorithm, based on the most recent European (SWF2018) and improved national level (SWLF2015) status databases, was developed and then applied to identify priority SWLF management regions and areas in Slovenia. In addition, a self-standing (LPIS independent) information, monitoring and decision support system has been designed for a target priority area (Goričko) in different spatial scales (from regional, 1 km, 300 m, AH to parcel level) to bypass existing gaps in SWLF-targeted CAP measure implementation strategies in Slovenia. Such results are urgently needed across all European countries to easier, faster and better realize CAP national strategic plans.

The Transformative Impact of Big Data in Healthcare: Improving Outcomes, Safety, and Efficiencies

The integration of Big Data analytics into the healthcare sector has initiated a period of unparalleled transformation, significantly influencing patient care, research, and healthcare administration. This detailed synthesis, derived from a systematic literature review of 31 articles published between 2013 and 2023, examines the extensive impact of Big Data within the healthcare industry. It addresses how Big Data enhances personalized medicine and optimizes treatment protocols, promotes the use of predictive analytics for the early detection and management of diseases, and bolsters patient safety through the implementation of real-time monitoring and decision support systems. The review also confronts the challenges and limitations associated with Big Data's application, including concerns over data privacy and security, ethical dilemmas, interoperability issues, and the necessity for both skilled personnel and advanced technological infrastructure. Furthermore, this overview ventures into future prospects and technological advancements, emphasizing the pivotal role of emerging technologies such as artificial intelligence (AI), machine learning, and blockchain in heralding a new era of healthcare. It highlights Big Data's instrumental role in supporting public health measures and preparing for pandemics, alongside forecasting the ongoing influence of Big Data in fostering innovative healthcare paradigms. This analysis advocates for a concerted effort among healthcare professionals, policymakers, and technology experts to fully leverage Big Data's capabilities in healthcare, aiming to enhance health outcomes, increase operational efficiencies, and ensure the long-term viability of healthcare systems.

Applying Machine Learning to Blood Count Data Predicts Sepsis with ICU Admission.

Timely diagnosis is crucial for sepsis treatment. Current machine learning (ML) models suffer from high complexity and limited applicability. We therefore created an ML model using only complete blood count (CBC) diagnostics. We collected non-intensive care unit (non-ICU) data from a German tertiary care centre (January 2014 to December 2021). Using patient age, sex, and CBC parameters (haemoglobin, platelets, mean corpuscular volume, white and red blood cells), we trained a boosted random forest, which predicts sepsis with ICU admission. Two external validations were conducted using data from another German tertiary care centre and the Medical Information Mart for Intensive Care IV database (MIMIC-IV). Using the subset of laboratory orders also including procalcitonin (PCT), an analogous model was trained with PCT as an additional feature. After exclusion, 1 381 358 laboratory requests (2016 from sepsis cases) were available. The CBC model shows an area under the receiver operating characteristic (AUROC) of 0.872 (95% CI, 0.857-0.887). External validations show AUROCs of 0.805 (95% CI, 0.787-0.824) for University Medicine Greifswald and 0.845 (95% CI, 0.837-0.852) for MIMIC-IV. The model including PCT revealed a significantly higher AUROC (0.857; 95% CI, 0.836-0.877) than PCT alone (0.790; 95% CI, 0.759-0.821; P < 0.001). Our results demonstrate that routine CBC results could significantly improve diagnosis of sepsis when combined with ML. The CBC model can facilitate early sepsis prediction in non-ICU patients with high robustness in external validations. Its implementation in clinical decision support systems has strong potential to provide an essential time advantage and increase patient safety.

Development of Decision Support System (DSS) for surface runoff and erosion control structures planning: a case study in the Upper Citarum Watershed & towards sustainability inland waters ecosystem

The watershed ecosystem is a complex system in which there are numerous transportation and transfer mechanisms of mass and energy. To make management more effective in all national critical watersheds, there needs to be more scientific, evidence-based policymaking that is based on an understanding of the system and mechanisms of the socio-hydrological processes of the watershed. The availability of Decision Support System (DSS) technology can be the appropriate approach to this need because DSS can be an interface between scientific and practical needs (easy-to-use, easy-to-access, user-friendly). However, the availability and implementation of DSS as an important tool in the optimal design of sustainable watershed management in Indonesia are still very limited. This study aims to create a prototype DSS that practitioners and policymakers can use to identify priority areas and optimize technical solutions for controlling surface runoff and soil erosion at various scales in the internal watershed. Herein, the spatial-based numerical modeling system and process mechanism; the database and knowledge; and the Graphical User Interface (GUI) are the three main components that have been used as a framework for model-based DSS development. A distributed rainfall-runoff-erosion model (EcoHydro) is the main engine of DSS for spatially quantifying dimensions of hydrological responses, erosion rate, and sediment production according to the user’s specifications and providing design options for control measures of them. The upper Citarum river basin in West Java, which is a critical and first national priority watershed, has been selected as the case study for DSS development and to demonstrate its application. Furthermore, the resulting DSS can later be developed for use in watersheds and other nationally critical lakes.

Enhancing Control Room Operator Decision Making

In the dynamic and complex environment of industrial control rooms, operators are often inundated with numerous tasks and alerts, leading to a state known as task overload. This condition can result in decision fatigue and increased reliance on cognitive biases, which may compromise the decision-making process. To mitigate these risks, the implementation of decision support systems (DSSs) is essential. These systems are designed to aid operators in making swift, well-informed decisions, especially when their judgment may be faltering. Our research presents an artificial intelligence (AI)-based framework utilizing dynamic influence diagrams and reinforcement learning to develop a powerful decision support system. The foundation of this AI framework is the creation of a robust, interpretable, and effective DSS that aids control room operators during critical process disturbances. By incorporating expert knowledge, the dynamic influence diagram provides a comprehensive model that captures the uncertainties inherent in complex industrial processes. It excels in anomaly detection and recommending optimal actions. Furthermore, this model is improved through a strategic collaboration with reinforcement learning, which refines the recommendations to be more context-specific and accurate. The primary goal of this AI framework is to equip operators with a live, reliable DSS that significantly enhances their response during process upsets. This paper describes the development of the AI framework and its implementation in a simulated control room environment. Our results show that the DSS can improve operator performance and reduce cognitive workload. However, it also uncovers a trade-off with situation awareness, which may decrease as operators become overly dependent on the system’s guidance. Our study highlights the necessity of balancing the advantages of decision support with the need to maintain operator engagement and understanding during process operations.

Decision Support System Detecting Patients at Risk of Prolonged QT and Associated Mortality.

Prolonged QT interval is an independent risk factor for all-cause mortality. However, evaluation of mortality associated to the implementation of a clinical decision support system to increase awareness and provide management recommendations has been challenging. Here we present our attempt to develop a model using only electronic data and different control groups.

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.

The Facilitators and Barriers of the Implementation of a Clinical Decision Support System for Breast Cancer Multidisciplinary Team Meetings-An Interview Study.

AI-driven clinical decision support systems (CDSSs) hold promise for multidisciplinary team meetings (MDTMs). This study aimed to uncover the hurdles and aids in implementing CDSSs during breast cancer MDTMs. Twenty-four core team members from three hospitals engaged in semi-structured interviews, revealing a collective interest in experiencing CDSS workflows in clinical practice. All interviews were audio recorded, transcribed verbatim and analyzed anonymously. A standardized approach, 'the framework method', was used to create an analytical framework for data analysis, which was performed by two independent researchers. Positive aspects included improved data visualization, time-saving features, automated trial matching, and enhanced documentation transparency. However, challenges emerged, primarily concerning data connectivity, guideline updates, the accuracy of AI-driven suggestions, and the risk of losing human involvement in decision making. Despite the complexities involved in CDSS development and integration, clinicians demonstrated enthusiasm to explore its potential benefits. Acknowledging the multifaceted nature of this challenge, insights into the barriers and facilitators identified in this study offer a potential roadmap for smoother future implementations. Understanding these factors could pave the way for more effective utilization of CDSSs in breast cancer MDTMs, enhancing patient care through informed decision making.

Enhanced Solutions to Intuitionistic Fuzzy Multiobjective Linear Fractional Optimization Problems via Lexicographic Method

Optimization involving fuzzy numbers generally, and intuitionistic fuzzy numbers particularly is more and more an essential part of any computational intelligence method; and might be of interest in modeling fuzzy control systems, or carrying out a fuzzy sensitivity analysis. The recent literature includes many research papers related to both theoretical modelling, and practical implementation of fuzzy decision support systems. Fuzzy optimization is one of the main part in such decisionmaking tools. A realistic solution to a fuzzy optimization problem is always desired, and similarly, a Pareto optimal solution to a multiple objective optimization problem is always a need. In this paper we analyze the shortcomings; and eliminate the weaknesses of a solution approach from the literature proposed by Moges et al. in 2023. Firstly, Moges et al. used an accuracy function to de-fuzzify the original intuitionistic fuzzy optimization problem; then linearized the obtained crisp problem; and finally involved fuzzy goals to solve the derived multiple objective linear problem. We present two faulty key points of their approach, namely the de-fuzzification and linearization steps; prove the inappropriateness of their results; and propose some improvements. The final aim in addressing the first key point is to clarify the border between defuzzifications made in accordance to true theoretical statements, and those made for modeling reasons. The methodological improvement we propose is related to the second key point and it assures that Pareto optimal solutions - highly required in multiple objective optimization - are obtained. To illustrate our point of view, we use a numerical example from the literature, and report better numerical results derived by the improved methodology.

Manufacturing Line Ablation, an approach to perform reliable early prediction

Manufacturing and assembly lines have become more and more complex over the years. One reason for this complexity is multistage manufacturing, where faults in one stage may carry forward downstream in the production line. Our goal is to find the production stage where reliable fault prediction will already be possible. Our approach takes an holistic view over the complete manufacturing line and performs an ablation study on product-specific data. The outcome of our approach is the analysis of a given manufacturing line that results in locating manufacturing stages where reliable prediction of faulty products is possible. Our objective is to support the implementation of an operative decision support system that allows to decide when to perform early prediction in a manufacturing process.

Application of Electre Method Decision Support System for Laptop Selection

In the contemporary era, laptops have transitioned from being perceived as luxury items to becoming an essential tool for work, education, and various learning activities. The market is saturated with a plethora of laptop manufacturers, providing consumers with a wide array of options to cater to their specific needs and budget constraints. However, a common challenge faced by consumers is the mismatch between their requirements and the specifications of available laptops, often due to a lack of understanding. Addressing this issue requires the implementation of a suitable Decision Support System (DSS). The proposed solution involves employing the Electre method, renowned for its multicriteria approach. This method systematically eliminates unsuitable alternatives, facilitating the identification of laptops that align with the desired criteria. The process involves several key steps, such as defining criteria and alternatives, assigning weights, normalizing the decision matrix, determining concordance and discordance indices, and ultimately establishing an aggregate dominance matrix. By leveraging the Electre method, this Decision Support System can effectively recommend laptops that not only meet the consumer's specified criteria but also align with their budget constraints. This systematic approach aims to enhance the overall decision-making process, ensuring optimal satisfaction for laptop buyers in terms of both performance and financial considerations.

Sustainability-Driven Green Innovation: Revolutionising Aerospace Decision-Making with an Intelligent Decision Support System

Green innovation refers to developing and implementing new technologies, practices, products, and processes that promote sustainability and reduce environmental impacts. This article postulates the conceptualisation and implementation of an intelligent decision support system (IDSS) tailored to the aerospace technology sector. The data were collected from open sources such as social media and analyzed using the natural language processing tool. The envisaged IDSS is a comprehensive and seamlessly integrated platform designed to undergird decision-making, problem-solving, and research initiatives within the aerospace industry. Catering to the sector’s engineers, technicians, and managerial cadres, it aims to unravel complex datasets, proffer incisive analyses, and furnish prudent advice and recommendations. Its multifaceted capabilities range from data search and optimisation to modelling and forecasting. With an emphasis on harmonious integration with extant aerospace systems, it strives to provide engineers and technicians with enriched data insights. Moreover, its design ethos is centred on user-friendliness, underscored by an intuitive graphical interface that expedites seamless access and utilisation. Ultimately, the envisioned IDSS will augment the aerospace industry’s analytical prowess and will serve as a potent instrument for effective decision-making.

Method of information technology for structure analysis of urban network fire-rescue units

The subject of this study is the process of analyzing the structure of the network of fire-rescue units of the city in the context of optimizing their spatial distribution. The purpose of this work is to increase the objectivity of decisions made while forming a city network of fire-rescue units by creating an information technology (IT) method for analysis of its structure based on the use of spatially distributed data. Objectives: to find ways to improve the level of fire safety, analyze existing approaches to the formation of a network of fire-rescue units, considering the peculiarities of building and organizing a network, and adapt the classical problem of placement; to propose a method for solving it to minimize the distance between the fire-rescue unit and the possible place of fire while ensuring maximum coverage of the territory by fire service; and to develop IT structure for its implementation based on the information flow model of the process of analyzing the fire-rescue units network using a geospatial approach. The following results were obtained. The study of the classical location problem and its adaptation to real problems arising from the analysis of the urban network of fire-rescue units made it possible to represent it as a set of independent complete bipartite graphs. To search the location of network nodes while solving an adapted problem, an IT method is developed, which, based on the p-median model, combines the author’s methodology for studying information processes and methods of geospatial analysis. Summarizing the requirements of the current legislation, a set of input and output IT data and a set of operations have been formed. The representation of the IT structure in the form of a data flow diagram explains how the set of factors is processed and generalized when making decisions on the creation and / or improvement of the existing city fire department. Conclusions. The results of the bibliographic search confirm the need to consider the spatial features of the area where it is planned to create a fire-rescue unit, as well as the spatial configuration of the urban network of existing fire stations, to evaluate its effectiveness using an integrated indicator. This requires the development of specialized methods focused on the use of geo-information systems for their implementation in decision support systems. Scientific and methodological support for IT has been developed, which gives local authorities a tool for analyzing fire safety in the city to create and / or improve the existing fire protection. An experiment to study the capabilities of the proposed method based on volunteered geographic information on Kharkiv city showed the effectiveness of its use for solving classical problems of placement, considering the accepted restrictions on the spatial availability of fire-rescue units. At the same time, additional opportunities appear in the formation of options for improving the network of fire-rescue units, considering their spatial distribution, workload, accessibility, and the resulting areas of coverage / non-coverage by the fire service. For example, the fire service coverage area of the existing structure of fire stations has been assessed. During the regulated time, it reaches 70% of the Kharkiv city area, and depending on the real road traffic, it can vary from 64.61% to 73.44%. It is illustrated that the creation of two additional fire and rescue units in the northern and southern parts of Kharkiv will increase the coverage area by approximately 5%, on average increasing it to 75.1%

Sistem Pendukung Keputusan Pemilihan Jurusan Menggunakan Metode TOPSIS Berbasis Web Studi Kasus: SMK Negeri 38 Jakarta Pusat

The selection of majors at SMK Negeri 38 Jakarta currently relies on a single opportunity during registration, without considering various factors that might influence students' choices. This underscores the necessity for developing a decision support system that can aid prospective students in selecting majors aligned with their interests, potentials, and characteristics. The aim of this research is to design and implement a decision support system using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method as an alternative to facilitate a more informed decision-making process. The research methodology involves data collection from prospective students, including personal interests, the average grades from the first to the fifth semester of junior high school, average school examination scores, registration pathways, and percentiles of grades. This data serves as input for the TOPSIS method to generate recommendations for study focus options that suit the characteristics of each prospective student. The research findings indicate that the implementation of a TOPSIS-based decision support system offers a more accurate possibility for major selection, aligning with the potentials and interests of prospective students. This system allows for data filtering and processing to produce recommendations guiding prospective students in choosing a suitable study focus among three available options: Hospitality, Culinary Arts, and Fashion Design. Conclusively, the decision support system using the TOPSIS method proves to be an effective solution in aiding new prospective students at SMK Negeri 38 Jakarta in selecting majors aligned with their interests and potentials, thereby reducing the tendency for errors in major selection based on a single registration opportunity.