Decision Support System For Evaluation Research Articles

Investigation of Bad Credit in P2P Lending Based on Personal Character in Indonesia

This research examines Indonesian P2P lending and bad loan management by analyzing borrower characteristics. The large value of problematic loans is a key signal for this investigation. The Big Five Personality Approach is used to survey P2P lending users. This study solely examines if the Big Five personality traits affect evaluation. On March 5–18, 2023, 17 P2P lending sites gathered data from 197 respondents for two weeks. Data validation continued until 11 respondents were pruned. The remaining 186 respondents' data was evaluated using SPSS. The management process yielded two hypothesis tests, the T-test and the F-test, which showed that the Big Five personality significantly affects present assessment activities. F-Test findings show the Big Five Personalities have a big impact concurrently. These two factors suggest that the Big Five Personalities may evaluate decision support systems for borrower recommendations.

Application of The Rapid Application Development Method to Develop a Decision Support System for Employee Performance Assessment in a State-Owned Electricity Company

This study aims to create a decision support system for employee performance evaluation that will help managers choose qualified applicants for advancement. In this study, observation, interviews, and literature review were employed as data collection techniques. RAD is the system development methodology that the author used in this study. Black box testing is used for testing. The following conclusions were drawn from the research's findings: first, it created a helpful decision support system that helped managers judge employee promotions by giving them more objectivity. This system helps managers make more accurate and equitable promotion decisions by offering rewards for evaluating employee performance. Second, this research contributes to the field by developing a decision support system visually representing employee performance metrics using graphs. Using the MFEP and MPE methods in the system provides a clear and easy-to-understand picture of employee performance. With graphs, managers can quickly and efficiently evaluate employee performance data, facilitating decision-making.

A Decision Support System for Employee Performance Evaluation under Uncertainty in Hotel Industry

A Decision Support System for Employee Performance Evaluation under Uncertainty in Hotel Industry

A decision support system for supplier quality evaluation based on MCDM-aggregation and machine learning

Evaluating suppliers’ quality performance is one of critical tasks of quality management since it is directly related to quality assurance, improvement and development, especially for manufacturing companies. However, the supplier quality evaluation remains challenging due to the involvement of a large amount of qualitative and quantitative data, multi-dimensional attributes, as well as a large number of items and suppliers, etc. In addition, while various multiple criteria decision making methods (MCDMs) exist and can be used for supplier quality evaluation, individual choice and application of the methods often result in inconsistent or conflicting evaluation results. To overcome the challenges, in this study, we develop a decision support system for supplier quality evaluation to deal with the practical complexity and to take advantage of the availability of the big industrial data. The system is presented as a conceptual framework including three modules: the decision matrix and criteria initialization module, the MCDM method selection and implementation module, and the aggregation module. The sequential conduction of each module formulates and solves the multiple supplier and item quality evaluation problem. The implementation of the system can be applied to dynamically monitor and evaluate supplier and item quality performance. Moreover, as the core intelligent part of the decision support system, in the aggregation module, we propose an aggregated MCDM model based on the machine learning concepts. It aims at providing more reliable evaluations across multiple suppliers and items. The aggregation model also contributes to the MCDM literature as an independent method which is not restricted to the purpose of supplier quality evaluation. The methods shed lights on how to construct and solve MCDM problems in the big-data era. Finally, the application of the decision support system is illustrated by a case of a large automotive company. The case shows that our decision support system provide more robust and reliable evaluation results than the traditional individual multiple criteria decision making method.

Decision Support System for Evaluation and Ranking of Robots Using Hybrid Approach

A hybrid approach namely “fuzzy modified distance based approach (FMDBA)” is proposed and presented for the selection and ranking of industrial robots, which is hitherto not applied in open literature for the purpose. The priority weights of the selection criteria/sub-criteria and performance ratings of the alternative robots for subjective selection criteria/sub-criteria are obtained on ten point and seven-point fuzzy scales using experts’ opinion. Statistical methods are used for ensuring reliability of the data obtained from experts. FMDBA method can incorporate interaction among selection criteria/sub-criteria into the decision analysis, an issue that has been generally overlooked in earlier robot selection studies. The robots are ranked according to the Euclidian distance of alternative robots termed as composite distance from the hypothetically imagined best possible robot. The methodology is illustrated through an example using a previously published data set of 27 robots based on four selection criteria. The results are compared with those of other multicriteria decision aids, which have been also employed in earlier studies addressing robot selection. A decision support system, “ROBORANK,” is developed, which is user friendly and also does not require an extensive technical knowledge of the selection criteria/sub-criteria and or robots for its use.

Emotion analysis-based decision support system for public perception evaluation in urban planning and design using social media text

Emotion analysis-based decision support system for public perception evaluation in urban planning and design using social media text

A data-driven decision support system for sustainable supplier evaluation in the Industry 5.0 era: A case study for medical equipment manufacturing

Effective supplier management is critical for an enterprise’s success, as supplier procurement accounts for up to approximately 70% to 80% of total manufacturing costs. Correct supplier selection can ensure the competitiveness of the enterprise and contribute to supply chain integration and innovation. In recent years, supplier evaluation frameworks based on Industry 4.0 concepts have contributed to the development of the industry. However, the novel concepts of Industry 5.0 require examination from a people-oriented and sustainable perspective. Unfortunately, at the present time, supplier evaluation frameworks based on Industry 5.0 are lacking. Therefore, the primary task of this study is to develop a novel and comprehensive supplier evaluation framework for the Industry 5.0 era. This study proposes a data-driven decision support system to execute the supplier evaluation process. First, variable precision- dominance-based rough set approach (VC-DRSA) is applied to extract the core criteria, to remove the noise factors and to generate decision rules for the decision-makers’ reference. Second, the criterion importance through intercriteria correlation (CRITIC) approach is adopted to obtain the dependency weights of the core criteria and their ranking. Finally, a modified classifiable technique for order preference by similarity to ideal solution (CTOPSIS) is used to integrate the final performance values of suppliers when new alternative suppliers are added. The research concept is in line with the conception of data-driven decision support in business intelligence and does not rely on the subjective judgments and opinions of experts. Data provided by a multinational medical equipment manufacturer are used as an example to demonstrate the proposed model. VC-DRSA retains nine core criteria from the original twenty criteria, which greatly reduces the labor and cost of supplier audits. In addition, the CRITIC results show that digital transformation, real-time information sharing, and organizational culture transformation are the three main factors affecting the development of enterprises towards Industry 5.0. The results show that CTOPSIS can be used to quickly assess the ratings of new alternative suppliers are listed.

Teacher Performance Evaluation Decision Support System Using Simple Additive Weighting: Case Study Mentari Intercultural School

The role of the teacher is critical in the process of developing a country's resources. In this case, efforts are required to maintain teacher quality. Evaluating teacher performance is one method of maintaining the quality of these teachers. However, many teacher performance evaluation processes are still carried out manually in Indonesian schools. The solution to this problem in this study is the creation of a teacher performance evaluation system in the form of a web-based Decision Support System prototype by applying Simple Additive Weighting. Several pieces of software, including Java, Hypertext Markup Language, PHP, and Cascading Style Sheets, are used in this study. As a result, this system contributes to the simplification of the teacher evaluation process.

Spatial decision support systems for effective ex-ante risk evaluation: An innovative model for improving the real estate redevelopment processes

The real estate redevelopment process is an important route for achieving the sustainable development goals established worldwide, but at the same time it represents a complex and not very transparent decision-making issue for the public and private subjects involved. In particular, for the private entrepreneurs it is generally considered more risky than new construction, therefore it requires a careful evaluation for avoiding losses. Most of the existent risk assessment tools provide for the analysis at the aggregated scales or require knowledge of many financial data of the project which are often not yet known in an ex-ante evaluation condition. Aim of the work is to define a structured framework for creating a Spatial Real Estate Risk Index (ISRR) through a spatial decision support system based on an innovative model that allows public and private subjects to carry out an effective ex-ante risk assessment at the sub-municipal territorial scale for public-private partnerships (PPP) risks. The proposed model adopts the flexibility of the Analytic Hierarchy Process multicriteria technique for managing qualitative and quantitative real estate data, the capability of indicators system to reduce the complexity of the real estate risk issues and the sleight of the Geographic Information System to clearly show the spatial distribution of the real estate risk. The ISRR is a territorial synthetic index that represents the “base risk”, i.e. the risk level that is expressed by the different features that characterize the demand and supply of the several urban areas within the city at the time of the evaluation. In order to test the usefulness of the proposed model, the application to the city of Rome (Italy) is described. The obtained results highlight the immediate ability to recognize the riskiest urban areas located on the northern and eastern boundaries of the city. The innovative contribution of the work is mainly represented by the analysis of the real estate risk carried out at the sub-municipal scale by using both quantitative and qualitative real estate data, therefore the proposed structured framework for creating the ISRR allows to immediately recognize the riskiest and least risky sub-municipal areas through an adequate risk map.

SISTEM PENUNJANG KEPUTUSAN PENILAIAN KINERJA KARYAWAN MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) PADA PT. LIEBRA PERMANA JAKARTA

Vanny Novianny (11212976), the Decision Support System for Employee Performance Evaluation Using the Simple Additive Weighting (SAW) Method at PT. Liebra Permana Jakarta
 The employee performance appraisal process is a complex process and requires careful consideration. Employee performance appraisal at PT. Liebra Permana Jakarta is carried out by looking at several criteria such as the value of Attendance, Performance, Discipline, and Initiative. However, the system at PT. Liebra Permana Jakarta is still manual, therefore the authors are trying to create a Decision Support System program in evaluating employee performance using the website-based PHP and MySQL programming languages. This system has a better advantage when compared to manual calculations. The method used in assessing employee performance is by using the Simple Additive Weighted (SAW). The results of this study apart from the DSS application website are calculations using the Simple Additive Weighted method, with reference to the criteria for Absence, Performance, Discipline and Initiative, normalization and ranking, the best employee with the highest score is Felicia.

Decision Support System for Customer Evaluation and Manager Assignment in the Defense Industry

Decision Support System for Customer Evaluation and Manager Assignment in the Defense Industry

A Decision Support System for the safety evaluation of urban pedestrian crossings

This paper proposes an innovative methodology, based on a Decision Support Systems, for the safety evaluation of pedestrian crossings without traffic lights in urban neighborhood areas. It provides an on-site inspection performed using ad-hoc data check lists, and it allows to assign a safety rate to the pedestrian crossing, in order to define a priority list of interventions and to suggest which features need to be improved. This new approach can be useful and easy to use for public administration managers and local governances, when they need to allocate limited financial resources to several pedestrian crossings. It has been applied to 10 pedestrian crossings on two roads in the urban area of Bologna and the resulted ranking list has been used by the Municipality of Bologna in its Urban Road Safety Plan 2016-2019.

Time to evaluate decision support systems for antimicrobial prescribing outside the hospital

Time to evaluate decision support systems for antimicrobial prescribing outside the hospital

Suitability modelling: an application to forest road development planning in Peninsular Malaysia

The development of modelling applications with Geographic Information System (GIS) has been widely used for planning and analysis. It is a vital technology that can be integrated with multi-criteria evaluation decision support system for problems solving process. This paper presents the model development for suitability forest road planning for the forestry department of Peninsular Malaysia’s application. Multiple significance preferences factors concerned with suitable forest road allocation were selected for the model development, such as slope constraint, elevation factor, the buffer zone of the river, and distance from the existing road. In conclusion, this model is developed to help forest managers sustainably manage the forest, particularly in commencing the forest harvesting operation, which has been widely reported as the main cause of negative environmental impact.

Categorized contrast enhanced mammography dataset for diagnostic and artificial intelligence research

Contrast-enhanced spectral mammography (CESM) is a relatively recent imaging modality with increased diagnostic accuracy compared to digital mammography (DM). New deep learning (DL) models were developed that have accuracies equal to that of an average radiologist. However, most studies trained the DL models on DM images as no datasets exist for CESM images. We aim to resolve this limitation by releasing a Categorized Digital Database for Low energy and Subtracted Contrast Enhanced Spectral Mammography images (CDD-CESM) to evaluate decision support systems. The dataset includes 2006 images, with an average resolution of 2355 × 1315, consisting of 310 mass images, 48 architectural distortion images, 222 asymmetry images, 238 calcifications images, 334 mass enhancement images, 184 non-mass enhancement images, 159 postoperative images, 8 post neoadjuvant chemotherapy images, and 751 normal images, with 248 images having more than one finding. This is the first dataset to incorporate data selection, segmentation annotation, medical reports, and pathological diagnosis for all cases. Moreover, we propose and evaluate a DL-based technique to automatically segment abnormal findings in images.

Toward a web-based multi-criteria decision support system for the layered evaluation of interactive adaptive systems

Toward a web-based multi-criteria decision support system for the layered evaluation of interactive adaptive systems

Wearable multi-sensor enabled decision support system for environmental comfort evaluation of mutton sheep farming

There is a tendency that the quality and safety of mutton sheep are paid more and more attention and environmental comfort directly affects it in the farming stage. However, the traditional environmental comfort evaluation is mainly based on the subjective experience of people or environmental factors, which is inaccurate, inefficient, untimely and costly. Therefore, environmental comfort decision supports system based on monitoring data from wearable sensors and ambient multi-sensors is considered an alternative and promising strategy. This paper aims to develop an wearable multi-sensor enabled decision support system (WMSE-DSS) for mutton sheep farming that integrates wireless monitoring of environmental factors and psychological factors, evaluation and prediction of environmental comfort to ensure the quality and safety of mutton sheep. In this research, mechanism of environmental comfort formation is analyzed to obtain key impacts, and the overall structure and function of the system are analyzed and designed according to the actual needs, environmental comfort evaluation model and environmental comfort level prediction model designed for grasping environmental comfort situation. To demonstrate the automatic monitoring and evaluation in this platform, experiments have been evaluated and analyzed. The results illustrate this system can reduce the potential risks, control environmental comfort accurately and provide farmers with automatic decision-making.

Decision Support System for Employee Performance Evaluation with Promethe Method. Case Study: Faculty of Engineering, Pancasila University

High competent human resources can support the level of employee performance. By conducting performance evaluation assessments it will be known the achievements of each employee. Assessment of employee performance evaluation carried out by the Faculty of Engineering, University of Pancasila uses criteria of diligence, teamwork, sincerity to work, skills, initiative, independence and attendance. The weights of criteria still determined by the Faculty authority. In this study, the authors used the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) to assist in making employee performance evaluation decisions, so that it can be seen which employees get the reward with good performance. There are six stages of the ranking including determine criteria and its weights, calculating the values of the requirements for each employee, preference value calculation between alternatives, calculating the value for the index, calculating entering flow, leaving flod and net flow. The data used is in the form of employee performance evaluation data that have range of 5 years consist of 16 samples taken from employee data from Faculty of Engineering, University of Pancasila. The calculation have been made using this six stages of the PROMETHEE method and after evaluation using precision approach have performance result of 96,364% accuracy compared with the conventional method

A Decision Support System for the Safety Evaluation of Urban Pedestrian Crossings

A Decision Support System for the Safety Evaluation of Urban Pedestrian Crossings

Fuzzy logic decision support system for hospital employee performance evaluation with maple implementation

In any organization, the capability, knowledge and skills play a significant role in success of an employee. For instant, in hospital management, the performance evaluation of an employee is mainly based on qualitative in nature. Evaluation of certain factors such as uncertainty, vagueness and imprecision is based on the decision making ability of the evaluator. The performance evaluation for promotion, incentives, bonuses, growth and development of any employee should be recognized in effective and efficiency manner with appropriate ratings. Therefore, this paper aims to design and implement a multi criteria performance evaluation for hospital employees to get promotion, incentives, bonus, growth and development. In this paper, we propose an Interval Valued Fuzzy Weighted Distance Algorithm (IVFWDA) for performance evaluation. The expected interval and the actual interval of the work achievement, management skill, personal quality, care, safety and risk management features are extracted by Interval Valued Fuzzy Soft Matrix (IVFSM) and it is effectively done using the proposed algorithm. An example is presented as a case study to illustrate the proposed algorithm. Implementation of the proposed algorithm in Maple is also discussed with sample computations.