Decision Support System Model Research Articles

Multi-criteria evaluation of reference terms requirements for development work performance

The specifics of carrying out research and development work (R&D), dictated by the requirements of legislation and standardization documents, impose a number of increased requirements on a potential contractor. The head of the organization, considering the possibility of performing R&D, makes a decision by analyzing the requirements of the terms of reference, the resources available to the organization and possible limitations. The article presents an approach to the formation of methodological support for assessing the possibility of implementing the requirements of the technical assignment for R&D within the time limits set by the contract, taking into account the resources available to the potential contractor and the level of qualification of personnel. To calculate the criteria, a modification of the PERT model is given and algorithms for analyzing the relevance of texts based on cosine similarity are applied. The proposed methodological apparatus can be used to create a model of a problem-oriented decision support system in preparation for R&D.

Integrated Decision Support System for Flood Disaster Management with Sustainable Implementation

Emergency management of a flood catastrophe should involve not only an immediate reaction to the flooding but also the pre-flood disaster phase, the flood disaster phase, and the post-flood disaster phase. This is in addition to the immediate response to the flooding. This research investigates whether or not a decision support system (DSS) for flood emergency management is necessary and whether or not it is feasible. Previously, we presented a model for the development of modular disaster management decision support systems. The prior work that we did on the integration of DSS model decomposition-derived modular subroutines is expanded upon in this research. The act of merging many models into a single comprehensive logical model is referred to as “Observed and Forecasted.” After that, it offers a systematic framework for handling crises throughout the whole of the process, and it concludes with an in-depth analysis of the function architecture of each application subsystem. In this work, we create a disaster management metamodel with the goal of eventually building a disaster management (DM) language. It will function as a representational layer of DM data that, over time, will create a system for DM decision support that mixes and matches various DM actions in line with the evolution of the disaster as it develops. A decision support system that will contain an innovative metamodel as a core component will be developed in order to accomplish the aims of integrating, enabling, and accelerating access to DM information. These objectives will be reached via the creation of a decision support system.

A Geographic Information System Base Spatial Decision Support Systems for Poverty Analysis and Management in Adamawa State, Nigeria

Poverty is a multidimensional, multifaceted, and complex, poverty can manifest in the social, political, economic, environmental and any sector of human existence. In Nigeria pervious government and present administration have adopted many poverty alleviations programs but most of them did not yield any positive result due to lack of spatial Geodatabase. Therefore, this article uses the concept of spatial decision support system and Geographic Information System in assessing the spatial distribution of poverty in Adamawa State as a model for decision making. Spatial decision making are multi-facetted challenges that needs technical requirement because they contain economic, social, environmental, and political dimension. Solving theses complex issues requires integrating and using information systems such as Geographic Information System and Spatial Decision Support Systems. The pragmatic approach was used as the methodology for collecting spatial information and for making inquiries into the complex phenomena. The geographic disparities theory was used as a lens to provide answers to the research questions. The Poverty situation in Adamawa State was measured using the Fosre, Greer and Thorbecke (FGT) index to determine the incidence of poverty in the state. A GIS base spatial decision support system model was design for decision making on how to alleviate poverty in disadvantage areas of the state.

MCDM Using Multi-Attribute Utility Theory and PIPRECIA in Customer Loan Eligibility Recommendations

Customer loan eligibility recommendations are an important step in the risk and financial assessment process. To ensure business continuity and customer satisfaction, it is important for financial institutions to consider several key factors. Problems in determining lending include various aspects that include credit risk that is difficult to assess appropriately, insufficient information about potential borrowers, potential fraud that can harm lenders. Regardless, financial institutions must ensure sound risk management and comply with applicable regulations and compliance standards to minimize potential losses and maintain fairness in decision-making related to lending. Cooperatives act as institutions that provide access to loans with more flexible terms than conventional financial institutions. The loan application process involves evaluating the customer's eligibility, which includes an analysis of financial condition, credit history, and repayment capacity. The contribution of this study provides a recommendation for savings and loan cooperatives in lending to customers using a decision support system model. The purpose of this study is to evaluate and compare customer loan feasibility using a combination of Multi-Criteria Decision Making (MCDM) methods, namely Multi-Attribute Utility Theory (MAUT) and Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA). This study aims to determine the ability of both methods to provide a comprehensive understanding of loan feasibility by considering a number of relevant criteria. The results of the customer loan eligibility rating recommend rank 1 with a final value of 0.5127 obtained by Customer K, rank 2 with a final value of 0.432 obtained by Customer I, and rank 3 with a final value of 0.3559 obtained by Customer F.

SISTEM PENDUKUNG KEPUTUSAN UNTUK KESESUAIAN LAHAN DAN TANAMAN PERTANIAN MENGGUNAKAN METODE PROFILE MATCHING

This study was made based on the body's requirements matching, because during this banya land cultivation cases that do not fit resulting in crop failures or even crop failure. The data used agricultural plant data consisting of rice, corn, soybeans and wheat. The right decision related to tersebuat is one of the determining factors in improving the agricultural sector.
 Determination of the appropriate plant based agriculture through three criteria: soil, climate and topography. Climate criteria has 4 sub-criteria which consists of precipitation, temperature, pH, and the intensity of the irradiation. This study shows how a decision support system profile matching can emberika recommendations on priorities for the agricultural crops of the land. In a profile matching methods are steps include finding the difference / gap, gap weighting, determine and calculate the value of core and secondary factor, calculate totals and calculates the ranking of land as a result. Interpolation calculations are also used in this system, which is to help find the value for the data used in the form of intervals. Interpolation is also used to help locate the weight values ​​gap gapnya values ​​in decimal form. From the results of this study demonstrate the benefits of the profile matching method as a model for decision support system that can help fatherly determine the appropriate agricultural crops occur ring on one particular area.

Multiple Attribute Decision Making Menggunakan Metode TOPSIS Dalam Penentuan Staff Marketing Terbaik

Multiple Attribute Decision Making (MADM) is an approach used in decision making to select the best alternative from a number of given criteria or attributes. This research aims to apply the TOPSIS method in selecting the best marketing staff so that it can become a reference and benchmark for companies in selecting the best marketing staff using a decision support system model. The results of the ranking of the selection of the best marketing staff who got rank 1 with a value of 0.644, namely Ahmad, rank 2 with a value of 0.539, namely Hermawan, rank 3 with a value of 0.529, namely Santoso, rank 4 with a value of 0.443, namely Jayanti, rank 5 with a value of 0.399, namely Heru.

A New Efficient and Privacy-Preserving Hybrid Classification model for Patient-Centric Clinical Decision Support System

A lot of focus has recently been placed on clinical decision support systems, that use advanced data mining methods to assist clinicians in making wise decisions. Along with increasing diagnosis accuracy, clinical decision support systems (CDSS) have the added benefit of speeding up diagnosis. Data security is crucial in this system. In this research, we offer EPPCD (Efficient and Privacy preserving Patient-centric Clinical Decision) support system to assist physicians in predicting illness risks of patients in a privacy-preserving manner. This system is proposed to solve the privacy difficulties present in the CDSS. The fine-grained access control enabled by the novel Double Encryption Ciphertext Policy Attribute-Based Encryption (DE-CPABE) technique is found to be a potential solution to this issue. In the proposed system, the past patients’ historical data are stored in cloud and can be used to train the hybrid Rotation Forest and AdaBoost classifier. Furthermore, extensive simulations used to evaluate performance show that our technology is capable of quickly and accurately determining a patient's disease risk while maintaining their privacy. The proposed system model is divided into five parties: Cloud Platform (CP), Trusted authority (TA), processing unit (PU), data provider (DP), and undiagnosed patient (PA).

Decision Support System For Admission Of New Employees With Weighting Method (Weight Product Model)

Firli Group is an e-commerce and digital marketing company. At every new employee recruitment, Firli Group is still manual or semi-computerized, making it difficult to determine prospective new employees. Therefore we need a system that can be petrified to facilitate decision making, namely by using the Weight Product Model (WPM). WPM is a model of Decision Support System (DSS), namely by ranking, this analysis is carried out with a sample of 5 people. Decision support system on the acceptance of new employees, is expected to help the company.

KAJIAN KARAKTERISTIK LAHAN UNTUK MENDUKUNG PENYUSUNAN MODEL DECISION SUPPORT SYSTEM (DSS) OPTIMALISASI LAHAN DI KABUPATEN PANGKEP


 The biophysical characteristics and environmental conditions of the land are required for the development of a Decision Support System (DSS) model for land optimization. The purpose of this study was to examine the characteristics of land cover, and the biophysical and environmental conditions of the land in Pangkep Regency. Suboptimal land identification and analysis of land and climate characteristics are carried out on the basis of secondary data obtained from satellite images, both land cover data in 2022, data in the form of three-dimensional DEM (Digital Elevation Model), and land characteristic data in the form of land system maps produced by the Geospatial Information Agency. The land cover in the Pangkep Regency area consists of fourteen types of land cover dominated by jungles, rice fields, and ponds. Flat topography with slopes of 0-2% and geological formations of alluvium and coastal deposits, Tonasa Formation, and Sekala Formation are dominant in Pangkep Regency. The dominant soil characteristic in this region is a lithosol soil type, soil depth more than 150 cm, soil pH is slightly acidic (5.6-6.5), clay texture, soil drainage criteria are inhibited and good, soil salinity in the range of 0.33%, C-Organic content with very low and low criteria, medium soil CEC, low total N value with a range of 0.1–0.21% and medium with 0.22–0.51%, Soil potassium content including very low criteria (<10 mg/100g) is in the range of 0.05 – 0.2 mg/100g and dominant 0.2 mg/100g, and rock abundance is 100+. The rainfall in this region is predominantly 2500 mm.th-1 and the average temperature is 23 - 32°C.

Adaptive Decision-Support System Model for Automated Analysis and Classification of Crime Reports for E-Government

Adaptive Decision-Support System Model for Automated Analysis and Classification of Crime Reports for E-Government

Digital twins and blockchain technology in the industrial Internet of Things (IIoT) using an extended decision support system model: Industry 4.0 barriers perspective

Blockchain technology (BT) can be considered a great help to the concept of digital twins (DT) as it can make sure of transparency, the immutability of data, decentralization of data storage, and peer-to-peer communication in industrial sectors. DT refers to the integrated multiphysics, multiscale, and probabilistic representation of a real-world physical component. The current study attempts to well envisage the ways blockchain can restructure and transform DTs to provide secure manufacturing, assuring compliance, traceability, quality, authenticity, and safety. However, to the adoption of DTs and BT in the industrial Internet of Things (IIoT), there are several barriers. To identify the key barriers, the present study discusses a survey approach through comprehensive literature and interviews with experts. To do so, this study introduced a novel approach using decision-making theory under the q-rung orthopair fuzzy set (q-ROFS) to analyze the identified barriers. This study developed a new methodology called q-ROF-subjective and objective weight integrated approach (SOWIA)-weighted aggregated sum product assessment (WASPAS). An empirical case study to assess the barriers to employing BT into DTs for IIoT implementation in the era of Industry 4.0 is taken. Also, comparative work and sensitivity analysis are discussed to demonstrate the advantage of the presented approach.

The Hydrodynamic Model Application for Future Coastal Zone Development in Remote Area

Indonesia is an archipelago country with a wealth of marine resources. However, local communities have not optimally utilized the use of natural resources, including those in the coastal zone of Central Sulawesi, Indonesia. This research goal is to identify the potential coastal areas for future development in the coastal zones, such as grouper floating net cage (FNC) culture, seaweed cultivation, and tourism areas. Thus, it is intended to develop the methodology of the hydrodynamic models for decision support systems (DSS) within the analysis hierarchy process. There are a total of 25 parameters criteria to calculate the potential future coastal zone development, including physics, water quality, and zoning properties. This DSS can serve as the foundation for instruction, knowledge, and application in developing rural coastal regions. Because of its breadth, this research endeavor is still ongoing. After calibration and verification, the initial study of the potential area of approximately 98,000 ha indicates that the model meets the accuracy requirement within the range of the root mean square error of approximately 0.184. Then, the outcomes of the hydrodynamic model simulation in DSS can be used as essential information for maritime development at this location. The outcomes demonstrate that the best areas for grouper FNC cultivation, seaweed cultivation, along with marine tourism are 6,163 ha, 91,000 ha, and 9,024 ha, respectively. It is expected that this research will contribute to sustainable future coastal zone development in the vicinity of Central Sulawesi, Indonesia. Doi: 10.28991/CEJ-2023-09-08-02 Full Text: PDF

Group decision support system model to determine supervisor lecturers for student creativity programs

A group decision support system (GDSS) model was created in this study by implementing the weighted product (WP) method and the borda method to determine prospective assistant or supervisor lecturers for student creativity programs (PKMs) that are routinely carried out every year at the Sriwijaya State Polytechnic. This study applies 5 criteria, including i) education level, ii) academic position, iii) group tenure, iv) lecturer certification, and v) achievement in the field of three pillars of higher education. The decision-makers in this study consisted of the head of the department (DM–1), the secretary of the department (DM–2), and the head of the study program (DM–3) where they carried out the decision-making process in groups. The WP method is used to make preferences independently of the decision-makers to determine the best alternative based on predetermined criteria. The borda method is currently used to aggregate the decision-makers to obtain the final result in the form of an alternative ranking. The results of this study are sufficient to be used as a reference in determining the supervisor lecturer for PKM activities at the Department of Computer Engineering, State Polytechnic of Sriwijaya.

Group decision support system model to determine supervisor lecturers for student creativity programs

A group decision support system (GDSS) model was created in this study by implementing the weighted product (WP) method and the borda method to determine prospective assistant or supervisor lecturers for student creativity programs (PKMs) that are routinely carried out every year at the Sriwijaya State Polytechnic. This study applies 5 criteria, including i) education level, ii) academic position, iii) group tenure, iv) lecturer certification, and v) achievement in the field of three pillars of higher education. The decision-makers in this study consisted of the head of the department (DM–1), the secretary of the department (DM–2), and the head of the study program (DM–3) where they carried out the decision-making process in groups. The WP method is used to make preferences independently of the decision-makers to determine the best alternative based on predetermined criteria. The borda method is currently used to aggregate the decision-makers to obtain the final result in the form of an alternative ranking. The results of this study are sufficient to be used as a reference in determining the supervisor lecturer for PKM activities at the Department of Computer Engineering, State Polytechnic of Sriwijaya.

Sistem Pendukung Keputusan Penentuan Barista Terbaik Menggunakan Rank Sum dan Additive Ratio Assessment (ARAS)

The selection of the best barista has become a crucial factor in guaranteeing a unique and satisfying coffee experience for connoisseurs. As an appreciation of the art of coffee making increases, barista selection no longer revolves around technical skills, but also on creativity, dedication, and interpersonal expertise. This study aims to apply the ARAS (Additive Ratio Assessment) method in determining the best barista using the Rank Sum weighting method, so that the results of the criteria weighting will be better in producing the best barista recommendations based on the decision support system model. The criteria used in selecting the best barista are barista skills, barista knowledge, experience, mastery of tools, and communication. Based on the ranking results using the ARAS method, it recommended rank 1 with a final value of 0.9483 obtained by Fandy, rank 2 with a final value of 0.9294 obtained by Abdur, and rank 3 with a final value of 0.8902 obtained by Delvin. The results of the test assessment using the blackbox testing model all the features of the best barista determination application are in accordance with their functions, the final results of application testing by 100% have worked according to the features made.

A New Method for Commercial-Scale Water Purification Selection Using Linguistic Neural Networks

A neural network is a very useful tool in artificial intelligence (AI) that can also be referred to as an ANN. An artificial neural network (ANN) is a deep learning model that has a broad range of applications in real life. The combination and interrelationship of neurons and nodes with each other facilitate the transmission of information. An ANN has a feed-forward neural network. The neurons are arranged in layers, and each layer performs a particular calculation on the incoming data. Up until the output layer, which generates the network’s ultimate output, is reached, each layer’s output is transmitted as an input to the subsequent layer. A feed-forward neural network (FFNN) is a method for finding the output of expert information. In this research, we expand upon the concept of fuzzy neural network systems and introduce feed-forward double-hierarchy linguistic neural network systems (FFDHLNNS) using Yager–Dombi aggregation operators. We also discuss the desirable properties of Yager–Dombi aggregation operators. Moreover, we describe double-hierarchy linguistic term sets (DHLTSs) and discuss the score function of DHLTSs and the distance between any two double-hierarchy linguistic term elements (DHLTEs). Here, we discuss different approaches to choosing a novel water purification technique on a commercial scale, as well as some variables influencing these approaches. We apply a feed-forward double-hierarchy linguistic neural network (FFDHLNN) to select the best method for water purification. Moreover, we use the extended version of the Technique for Order Preference by Similarity to Ideal Solution (extended TOPSIS) method and the grey relational analysis (GRA) method for the verification of our suggested approach. Remarkably, both approaches yield almost the same results as those obtained using our proposed method. The proposed models were compared with other existing models of decision support systems, and the comparison demonstrated that the proposed models are feasible and valid decision support systems. The proposed technique is more reliable and accurate for the selection of large-scale water purification methods.

Developing a Decision-Making Support System for a Smart Construction and Demolition Waste Transition to a Circular Economy

This research work aimed to develop a decision support system (DSS) to select the most appropriate concrete waste management method, which is the most critical issue in the construction industry. The research process began with the study of the current situation of concrete waste management problems. Related theories and literature were reviewed, and experts were interviewed in depth. After that, the Delphi technique and the fuzzy analytic hierarchy process (FAHP) were used to analyze the decision-making structure and consider factors related to the waste management methods. Then, the FAHP process was analyzed, calculated, and prioritized using MS Excel until the results were obtained. Finally, decision structures were shown, evaluated, and prioritized using a case study by a group of experts belonging to the Thai circular economy construction industry (CECI). The contribution of this research line in the DSS model is by analyzing, calculating, and finding the most appropriate alternative solution for the construction waste industry. Limitations, recommendations, and future research directions are also presented.

Bias Analysis in Healthcare Time Series (BAHT) Decision Support Systems from Meta Data.

One of the hindrances in the widespread acceptance of deep learning-based decision support systems in healthcare is bias. Bias in its many forms occurs in the datasets used to train and test deep learning models and is amplified when deployed in the real world, leading to challenges such as model drift. Recent advancements in the field of deep learning have led to the deployment of deployable automated healthcare diagnosis decision support systems at hospitals as well as tele-medicine through IoT devices. Research has been focused primarily on the development and improvement of these systems leaving a gap in the analysis of the fairness. The domain of FAccT ML (fairness, accountability, and transparency) accounts for the analysis of these deployable machine learning systems. In this work, we present a framework for bias analysis in healthcare time series (BAHT) signals such as electrocardiogram (ECG) and electroencephalogram (EEG). BAHT provides a graphical interpretive analysis of bias in the training, testing datasets in terms of protected variables, and analysis of bias amplification by the trained supervised learning model for time series healthcare decision support systems. We thoroughly investigate three prominent time series ECG and EEG healthcare datasets used for model training and research. We show the extensive presence of bias in the datasets leads to potentially biased or unfair machine-learning models. Our experiments also demonstrate the amplification of identified bias with an observed maximum of 66.66%. We investigate the effect of model drift due to unanalyzed bias in datasets and algorithms. Bias mitigation though prudent is a nascent area of research. We present experiments and analyze the most prevalently accepted bias mitigation strategies of under-sampling, oversampling, and the use of synthetic data for balancing the dataset through augmentation. It is important that healthcare models, datasets, and bias mitigation strategies should be properly analyzed for a fair unbiased delivery of service.

Development of PROMETHEE-Entropy data mining model for groundwater potentiality modeling: a case study of multifaceted geologic settings in south-western Nigeria

This work looks at developing an object-driven decision support system (DSS) model with the goal of improving the prediction accuracy of the present expert-driven DSS model in assessing groundwater potentiality. The database of remote sensing, geological, and geophysical information was constructed using the technological efficiency of GIS, data mining, and programming tools. Groundwater potential conditioning factors (GPCF) extracted from the datasets include lithology (Li), hydraulic conductivity (K), lineament density (Ld), transmissivity (T), and transverse resistance (TR) for groundwater potentiality mapping in a typical hard rock multifaceted geologic setting in south-western Nigeria. A Python-based entropy approach was used to objectively weight these factors. The weightage findings determined that the greatest and lowest given values for Ld and K were 0.6 and 0.03, respectively. The produced Python-based PROMETHEE-Entropy model algorithm was born through combining the weight findings with the Python-based PROMETHEE-II method. The groundwater potentiality model (GPM) map of the area was created using the model algorithm's outputs on the gridded raster of GPCF themes. Based on the suggested approach, the validated results of the created GPM maps using the Receiver Operating Characteristic (ROC) curve technique yielded an accuracy of 86%. An object-driven DSS model was created using the approaches that were used. The created object-driven model is a viable alternative to existing approaches in groundwater hydrology and aids in the automation of groundwater resource management in the research region.

Ensuring Information Security of OLAP-Containing Distributed Electronic Systems using Blockchain Technology

The article outlines the properties of blockchain technology, according to which the content of blockchain blocks is immutable and the use of a peer-to-peer (P2P) network implies decentralized management. It describes the essence and meaning of smart-contracts and smart-views derived from them, which are involved in the process of data analysis and decision making. The article considers the papers devoted to various approaches to the problem of integration of blockchain technology and data storage. To ensure all three key aspects of information security — the integrity, availability and confidentiality of information, which is important for both government and corporate structures, the article proposes to use a private blockchain, instead of a public one. A modification of the model of an electronic decision support system containing an OLAP block, which includes a private blockchain, is developed. It is shown that this DSS model can be used as an electronic demographic DSS, which is an infrastructure for solving state problems in this area, since many of the characteristics of this model correspond to the multisectoral nature of demography.