Preference Ranking Organization Method For Enrichment Evaluations Method Research Articles

Enhancing information fusion and feature selection efficiency via the PROMETHEE method for multi-source dynamic decision data sets

With the surge in big data, the complexity of synthesizing information from multiple sources has become a critical challenge for feature selection methodologies. Feature selection is the process of reducing the number of attributes in data. Traditional single-source centric approaches are inefficient, requiring extensive preprocessing for multi-source data consolidation prior to feature selection. At the same time, an information fusion method is needed to transform the multi-source information system with selected features into a single-source information system. This paper introduces a novel multi-source information fusion and feature selection approach that seamlessly integrates the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) with a dynamic adaptation mechanism. This method is adept at addressing the complexities introduced by the evolving nature of feature and information source dimensions. The Attribute Evaluation Matrix (AEM) and the Attribute Preference Degree Matrix (APDM) are proposed to systematically assess and rank the significance of attributes within a static decision-making framework. Following this, an information fusion method using the source center is proposed. The dynamic feature selection and information fusion methods are proposed to deal with the condition when number of attributes and samples change. Extensive experimental validation confirms that this method not only reduces the computational overhead associated with multi-source feature selection but also significantly enhances the efficiency as the volume and variety of data sources increase.

Enhancing Polybutylene Terephthalate (PBT) Composites: A PROMETHEE-Based Comparative Study of Reinforcement Materials

Polybutylene terephthalate (PBT) composites hold significant potential across various industrial applications due to their desirable mechanical, thermal, and electrical properties. In this study, we utilized the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) method to evaluate six alternative reinforcement materials for PBT composites: Glass Fibers (GF), Carbon Fibers (CF), Natural Fibers (NF), Carbon Nanotubes (CN), Nano-clay Particles (NP), and Aramid Fibers (AF). The evaluation parameters considered were Tensile Strength (MPa), Flexural Strength (MPa), Thermal Conductivity (W/mK), Electrical Conductivity (S/m), and Cost ($). Through comprehensive analysis, Carbon Nanotubes emerged as the top-ranked reinforcement material, exhibiting exceptional performance across all evaluation parameters. With high Tensile Strength, Flexural Strength, and Thermal Conductivity, combined with significant Electrical Conductivity, Carbon Nanotubes demonstrated their suitability for demanding applications. Additionally, while the Cost factor was comparatively higher, its superior performance justifies the investment. Conversely, Natural Fibers received the lowest rank among the alternatives. Despite potential advantages in cost-effectiveness and environmental sustainability, Natural Fibers exhibited inferior mechanical and thermal properties compared to other materials. Their low Tensile Strength, Flexural Strength, and negligible Electrical Conductivity highlight limitations in performance for many industrial applications. This study provides valuable insights for engineers and material scientists in selecting suitable reinforcement materials for PBT composites based on specific performance criteria. The PROMETHEE method offers a systematic approach to decision-making, facilitating informed choices in material selection for diverse applications. Future research could explore optimization strategies and further investigate the properties and potential applications of emerging reinforcement materials for PBT composites.

Optimizing Drug Delivery Vehicle with Multi-Criteria Decision Making (MCDM) - Based Excipient Selection

Excipients are used in drug delivery systems as a means of effectively delivering drugs to their target site. Multi-criteria decision-making (MCDM) methods are tools for decision-making that consider multidimensional factors. Such methods are a comparative technology used in medicine that combines individual criteria into the total assessment of selected alternatives. This study aims to enhance the solubility and bioavailability of drugs through the application of MCDM-based excipient selection. By incorporating the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), various excipients can be evaluated and ranked based on their suitability for specific applications, considering parameters related to drug solubility and bioavailability. The results highlight the potential of cyclodextrins (net flow: 0.0023) and Eudragit polymers (net flow: 0.0016) as preferred options for drug carriers, while Poloxamer 188 (P188) (net flow: -0.0030) is identified as the least preferred option. This study demonstrates the effectiveness of the PROMETHEE method in improving the performance of poorly soluble and bioavailable drugs, ultimately contributing to the development of new drug delivery systems. The findings have significant implications for therapeutic outcomes in the treatment of diseases.

MCDM-AHP and PROMETHEE methods integrated for base service strategy vendor evaluation and selection

Business competition is very important in controlling product-savvy customers. Strong basic service techniques will be the main factor that binds vendors as the final destination in the supply chain through the strength of business network processes. This research aims to create a strategic basis for evaluating and selecting vendors through the integration process services of the multi-criteria decision-making method analytic hierarchy process (MCDM-AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE) methods. Empirical studies show how this approach can provide optimal decision support for the vendor evaluation and selection process. Eight different types of criteria are required in its apps and must be realized as a barometer of the strategic basis for selecting vendors so that business processes are of high quality. These criteria include quality of goods, payment methods, payment terms, minimum transactions, discounts, delivery times, inventory, and service. The optimal weight for each criterion will be determined based on its importance to the synthesis process and its feasibility tested using mathematical algebra matrices and expert choice apps. Decision-making was based on the results of ranking evaluation of selected vendors through the development of 342 preference matrices, ten vendors were deemed worthy of acceptance and nine other vendors were rejected.

Water and Environmental Resources: A Multi-Criteria Assessment of Management Approaches

The present study applied a multi-criteria analysis to evaluate the best approach among six theoretical frameworks related to the integrated management of water–environmental resources, analyzing the frequency of multiple management criteria. The literature review covers the period from 1990 to 2015, with a notable presence of the theoretical frameworks of Integrated Water Resources Management (IWRM), Ecohealth, Ecosystem Approach (EA), Water Framework Directive (WFD), and, to a lesser extent, the Watershed Governance Prism (WGP) and the Sustainability Wheel (SW). The multi-criteria decision-making (MCDM) methods applied include AHP (Analytic Hierarchy Process), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), and PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations). Twenty-five criteria were analyzed, such as governance, participation, sustainability, decentralization, and health and well-being, among others. We started with five criteria for evaluating the hierarchy of the six theoretical frameworks using the AHP method. Subsequently, we again evaluated the five criteria using the TOPSIS and PROMETHEE methods to calibrate the results with the AHP. Then, using word counting, we evaluated the best approach, applying 10, 15, 20, and 25 more criteria. Our results indicate that the best integrated management alternative was the WFD, which fulfilled 47% of the management criteria. Second, with 45%, was the WGP, and third was IWRM, with 41%; less successful approaches to the criteria were demonstrated by the EA, SW, and Ecohealth methods. By applying this methodology, we demonstrated an excellent structured tool that can aid in the selection of the most important issue within a given sector.

A Combined AHP-PROMETHEE Approach for Portfolio Performance Comparison

Comparing portfolio performance is complex due to the fact that each model is dominant in its own risk space. Since there is no single dominant performance measure, the research problem is how to incorporate several different measures into a performance evaluation model that allows portfolios to be ranked. In this regard, the objective of this study was to develop a new comprehensive method for comparing portfolio performance based on multiple-criteria decision-making (MCDM). This paper proposes an integrated approach for stock market decision making that combines the Analytic Hierarchy Process (AHP) and the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), which allow hierarchical evaluation of a finite number of alternatives according to different criteria. This hybrid approach is especially advantageous, utilizing the strengths of both individual methods. AHP enables the decomposition of a complex problem into its constituent parts and the determination of weights for criteria, while the PROMETHEE method allows the investor to determine the preference function, complete ranking, and analysis of the robustness of the results. For the MCDM model in this study, different dimensions of performance measures are considered criteria: return measures, risk measures, stability measures, and predictability measures. The methodology has been applied in comparing real portfolios selected on the basis of different risk measures. For this purpose, weekly return data were used for a sample of stocks that are components of the STOXX Europe 600 Index for the period 2000–2020. In addition, a sensitivity analysis is performed to investigate the strength of the results of this method. It suggests that the simultaneous consideration of different performance measures and the investor’s attitude towards the importance of these measures are notably important in the portfolio efficiency estimation process.

IMPLEMENTASI METODE PROMETHEE PADA PEMILIHAN REGISTRAR DOMAIN DI INDONESIA

A domain registrar is a commercial company whose job is to register domain names that have been registered with ICANN (Internet Corporation for Assigned Names and Numbers) or maintain domain ccTLDs that involve certain country codes. Domain registration is the first step in website development as an effort to the existence of a business entering the digital economy era. To ensure the continuity of a business in the internet world, the selection of a domain registration service is very important. Because the domain is the identity as well as the address of a business on the internet. Domain registration service organizations must meet various important criteria needed as a professional domain registration service. These criteria must be able to answer the crucial characteristics of a domain registrar. This study uses PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation). The PROMETHEE method is used to rank among all alternatives based on predetermined criteria. The stages of problem solving in this study are determining alternatives, determining criteria, to ranking alternatives, so that will produce the best alternative output. In this study the PROMETHEE calculation results obtained with the best alternative output being the Domainesia registrar with a net flow value of 0.592.

A PROMETHEE based outranking approach for the construction of Fangcang shelter hospital using spherical fuzzy sets

This study mainly aims to develop two effective and practical multi-criteria group decision-making approaches by taking advantage of the ground-breaking theory of PROMETHEE family of outranking methods. The presented variants of Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method are acknowledged to address the complex decision-making problems carrying the ambiguous information, expressible in terms of yes, no, abstinence and refusal, owing to the preeminent condition and wider structure of spherical fuzzy sets. Both of the proposed approaches seek help from the Shannon’s entropy formula to evaluate the object weights of the decision criteria. The proposed techniques operate by taking into account the deviation between each pair of potential alternatives in accordance to different types of preference functions to determine the preference indices. The proposed technique of spherical fuzzy PROMETHEE I method carefully compares the positive and negative outranking flows of the alternative to get partial rankings. In contrast, the spherical fuzzy PROMETHEE II method has the edge to eliminate the incomparable pair by employing the net outranking flow to derive the final ranking. The application of proposed approaches is explained via a case study in the field of medical concerning the selection of appropriate site to establish Fangcang shelter hospital in Wuhan to treat COVID-19 patients. The convincing comparisons of the proposed methodologies with q-rung orthopair fuzzy PROMETHEE and spherical fuzzy TOPSIS methods are also included to verify the aptitude of the proposed methodology.

DECISION SUPPORT SYSTEM FOR SOCIAL ASSISTANCE’S BENEFICIARIES USING AHP – PROMETHEE METHOD IN KELURAHAN KARANGANYAR GUNUNG

Indonesia as a developing country has problems related to poverty. The government tries to overcome the problem of poverty from year to year by launching various aid programs, one of which is the Family Hope Program (PKH). The distribution of PKH social assistance (bansos) can actually help millions of beneficiaries every year, but there are also obstacles related to the lack of precise targeting of beneficiaries, which often occurs because there is no definite standard in the hierarchy of criteria for beneficiaries. Using the Decision Making System (SPK) with the Analytical Hierarchy Process (AHP) - Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method can provide an alternative to prioritizing aid recipients to assist decisions made by officers and related office holders. The AHP method is used to weight the poverty criteria, while the PROMETHEE method is used to rank the priority of social assistance recipients. Using 45 data on prospective PKH social assistance recipients in Karanganyar Gunung Village who were selected based on 14 poverty criteria, the Central Statistics Agency (BPS) resulted in a priority ranking of social assistance recipients. The ranking is based on the netflow value, the higher the value, the higher the priority level. The ranking using this method gives results in the form of residents with the first priority of receiving social assistance with a net flow value of 0.342 owned by Sumirah followed by Meliyani Kusrini with a net flow value of 0.325.

Evaluation of papers according to offset print quality: the intuitionistic fuzzy based multi criteria decision making mechanism

PurposeThe aim of this paper, using the intuitionistic fuzzy (IF)–set based Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method, is to create multicriteria decision-making (MCDM) mechanism that evaluates the papers according to the offset print quality, separately for each of the CMYK colors. Thanks to the mechanism, the most suitable and the most unsuitable paper for printing is determined.Design/methodology/approachWith the IF PROMETHEE method, respectively, deviations are calculated by linear criteria type; the overall IF preference relation matrix is obtained. Finally, the positive and negative outranking flows are calculated; by obtaining net outranking flows, all papers are ranked from best to worst for printability.FindingsBased on the results of the MCDM algorithm; the best offset printing quality is Matte Coated 115 gr. for cyan, magenta and black and Glossy Coated 115 gr. for yellow. The worst offset printing quality for all papers is 70 gr. III. low grade paper. According to the findings, when all colors are examined, the best paper for offset printing quality is coated papers; the worst paper is III. low grade papers.Originality/valueThe creation of mechanism that evaluates all criteria together leads to the most accurate result. Cases of hesitation are also addressed using an IF-based algorithm; all criteria were assigned individual importance levels, and a single result was obtained by activating all of the criteria simultaneously. Therefore reasons, this is paper that will bring innovation and shed light on studies in this field.

Selection of Suppliers in Industrial Manufacturing: A Fuzzy Rough PROMETHEE Approach

In supply chain management (SCM), the selection of suppliers plays a vital role in an efficient production process. Over the last few years, to form a trade-off between the quantitative and qualitative criteria, the selection of suppliers in SCM is considered very conclusive. The decisions generally demand different criteria to balance between every possible inconsistent parameters involving subjectivity and uncertainty in the process. The evaluation information mostly depends on the experience and knowledge of experts that are unsure and indistinct. This study introduces a novel decision-making method by integrating rough approximations with fuzzy numbers and preference ranking organization method for enrichment evaluation (PROMETHEE) to deal with subjective and objective vagueness in the assessment of decision makers. To minimize the dependency on experts’ judgements, entropy weights are computed from the original data set. The preference index is then computed using entropy weights and deviations among alternatives. The alternatives are ranked using the intersection of both positive flow and negative flow. To show the significance and importance of fuzzy rough PROMETHEE method, a case study of supplier selection in industrial manufacturing is discussed in detail. The developed method is effectively used to rank the supplier alternatives under given criteria. The results are then compared with different rough numbers and fuzzy numbers based on MCDM methods. The fuzzy rough PROMETHEE method can be efficiently used for the selection of the best suppliers to reduce losses and maximize the production process.

Nested information representation of multi-dimensional decision: An improved PROMETHEE method based on NPLTSs

In the increasingly complex environment of today, decision-making problems face general challenges involving the multi-dimensional relationships and the representation of uncertain information regarding performances, requirements, and designs. In this regard, this study combines the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method with the nested probabilistic linguistic term set (NPLTS) to rank the alternatives considering ease of use, cognitive information, and decreased complexity. We propose transformation functions with four kinds of NPLTSs to measure information scientifically, establish an NPLTSs-based PROMETHEE method, and apply it to a case study concerning the selection of marine fuels. The results show that renewable fuels are the preferred trend for marine transportation fuels, and the impact on the ranking of marine fuels is climate change (39.35%), technical availability (39.15%), cost (34.52%), and safety (18.55%). The proposed method considers cognitive-based multi-dimensional factors and makes full use of assessment information so that it is stable and suitable for complex and uncertain decision-making problems, and decision-makers can adjust the weights according to their preferences to obtain more reliable results. This study provides a novel method to deal with multi-dimensional decisions at both the theoretical and practical levels.

Uncertainty of Preferences in the Assessment of Supply Chain Management Systems Using the PROMETHEE Method

The use of Supply Chain Management (SCM) systems allows for the improvement of an organization’s operations. Companies use many Enterprise Resource Planning (ERP) systems that also include SCM functionalities. As a result, the selection of the right system to be used in the enterprise is a complex problem. The use of multi-criteria decision aid (MCDA) methods provides the possibility of system ordering in a ranking, based on an asymmetric preference relation, symmetric indifference and incomparability relations. The aim of the article is to evaluate ERP systems in terms of their support for SCM. The scientific contribution of the article is the study of the impact of various degrees of uncertainty of the decision-maker’s preferences on the evaluation results and the analysis of the impact of various approaches to the preferences of alternatives on the final ranking. An approach based on MCDA Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) was used for the study. The decision model uses 12 criteria, three different preference functions and two variants of the PROMETHEE method (I and II). In this way, a total of six rankings were built, and each of them includes seven systems, supporting the management of the supply chain. As a result of the study, it was found that the highest functionality in logistics is characterized by the Oracle E-Business Suite system, which is more functional than SAP ERP and JD Edwards EnterpriseOne. The remaining analysed systems offer much less functionality. The applied approach, which was possible with the use of various preference functions, allowed three different levels of uncertainty in the preferences of decision-makers to be taken into account in the study. Moreover, the application of two different variants of the PROMETHEE method made it possible for the obtained solution to take into account the uncertainty of positions taken by individual ERP systems in the final rankings.

Electric bus charging station site selection based on the combined DEMATEL and PROMETHEE-PT framework

Electric buses (EBs) are one of the most promising transportation modes to reduce air pollution and greenhouse gas emissions. With the promotion of EBs, the selection of EBs charging station (EBCS) site plays a key role in bus electrification. In order to deal with the information uncertainty and the complicated logical relationship between evaluation criteria, this paper establishes a combined framework of fuzzy decision-making trial and evaluation laboratory (DEMATEL), preference ranking organization method for enrichment evaluations (PROMETHEE) and prospect theory (PT) to evaluate EBCS sites. Firstly, the evaluation criteria system consisting of transportation, economy, technology, and environment for the EBCS site selection problem is established. Secondly, the fuzzy DEMATEL method is adopted to calculate the weights of EBCS evaluation criteria and analyze their causal relationships. Thirdly, the fuzzy PROMETHEE method with PT is combined to obtain the ranking of the alternative EBCS sites. Finally, based on the case study in Nanjing city, the proposed combined framework proves to be effectiveness and scalability. The numerical results show that the long-term influencing criteria (e.g., coordination with power grid planning, total investment cost), the net cause criteria and the criteria with strong mutual influence have great impact on EBCS site selection.

Multi-objective feature selection based on quasi-oppositional based Jaya algorithm for microarray data

Feature selection is the highly sought-after pre-processing technique for microarray data classification. Microarray data is a phenomenally imbalanced, high-dimensional dataset which requires a specifically designed feature selection technique. A hybrid feature selection (FS) technique is designed in this work for microarray datasets. The proposed FS technique has two phases. In the first phase, aggregate ranking of five well-known filter methods is obtained using the PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluations) method and is used to remove the unwanted features. The features selected in the first phase are then passed on to the second phase for further refinement. In the second phase, the quasi-oppositional-based multi-objective Jaya algorithm is designed for wrapper-based feature selection. Moreover, a novel quasi-opposite number generation technique for binary numbers is proposed. Ten benchmark microarray datasets are used to evaluate the performance of the proposed technique. Seven other state-of-the-art feature selection techniques are used for comparison and the proposed technique is found to outperform other techniques in terms of classification error rate and the selected number of features. In addition, the proposed FS technique is found to be ten times faster than other FS techniques.

A novel multicriteria decision‐making approach with unknown weight information under q‐ rung orthopair fuzzy environment

The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method has been developed as one of the effective techniques to accomplish the best alternative selection of multicriteria decision-making (MCDM) problems. However, the existing PROMETHEE methods fail to adjust the representation range of uncertain information. Moreover, the weight determination and information aggregation in the PROMETHEE model still suffer from an excessive dependence of decision makers' (DMs') subjective judgments and the lack of considering the interrelationship among criteria. To solve the aforementioned drawbacks and provide DMs with a reliable and robust method of MCDM, a novel PROMETHEE method based on the q-rung orthopair fuzzy sets, that is, q-ROF-PROMETHEE model, is presented herein. First, the PROMETHEE-II method is extended to q-rung orthopair fuzzy (q-ROF) environment, which not only handles the uncertainty of human cognition but also provides DMs with a broader space to represent their decisions information. Second, a new optimization model is constructed based on the proposed cross entropy and q-ROF entropy, which is capable of leveraging the preference of DMs to obtain the optimal objective weights among criteria. In addition, the q-ROF weighted Hamy mean operator is embedded in the preference information aggregation of the PROMETHEE method so as to deal with the interrelationship among criteria. Finally, q-ROF-PROMETHEE is applied to a hospital performance evaluation case to demonstrate the proposed method's superiority, validity, and feasibility.

MAKING AN APPLICATION FOR DETERMINING THE RANKING OF IMMIGRATION POLYTECHNIC STUDENTS WITH THE WEBSITE-BASED PROMETHEE METHOD

In the process, based on the ranking of the Immigration Polytechnic cadets, the number of considerations objectively indicators or criteria that have been determined. These indicators and criteria have weight values that will be analyzed quickly and structured. So we need a system to help the Immigration Polytechnic make a list of these people or multi-criteria. This study uses the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) as a Decision Support System (DCS). The PROMETHEE method is one of the groups in the Multi-Criteria Decision Making (MCDM), which will analyze using many indicators or multi-criteria. It will produce output in the form of decision recommendations to its users. In addition, the PROMETHEE method will compare the value of one alternative with another alternative and produce a ranking based on the value that Immigration Polytechnic students get. Researchers conducted two tests by comparing the results of the test data and the results of the system. The tests are accumulated and calculated using the formula for the estimation of the confusion matrix. The final score is 90% which is included in the Excellent Classification and Best Classification.

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

Review of PROMETHEE method in transportation

Abstract In recent decades, decision support system has been constantly growing in the field of transportation planning. PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluation) method is an efficient decision-making support deployed in case of a finite number of criteria. It provides a partial ranking through PROMETHEE I and a complete ranking with PROMETHEE II. This outranking methodology is characterized by the elimination of scale effects between criteria and managing incomparability with the comprehensive ranking. However, PROMETHEE does not provide guidance to assign weights to criteria and assumes that decision makers are able to allocate weights. This review presents an overview of PROMETHEE models applied in transportation and points out the found gaps in literature.

The issue in Indonesian palm oil stock decision making: Sustainable and risk criteria

The palm oil industry has a strategic role in economic development in Indonesia, especially in alleviating poverty and creating other businesses that can support the industry. Operational activities in the palm oil industry are closely related to environmental issues (deforestation, land-use change, and air pollution) and social conflict. The certification program is an effort for the palm oil industry to implement sustainable development. The certified palm oil industry will increase industrial profitability in the long run to increase investor interest in the future. The decision to choose palm oil industry stocks that carry out sustainable practices and generate maximum returns is an exciting issue, but how investors can choose the right stocks and the minimum risk level. This study aims to apply the decision-making model to choose the optimal stock in the palm oil industry, which involves sustainable certification and risk criteria. The method used in this study was the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) based on the Indonesia Stock Exchange (IDX) data. Determinants of stock selection decisions from previous research are considered criteria for decision making. Through the PROMETHEE method, a list of the rankings of the oil palm industry shares can be generated. The sustainable certification and risk criteria can be used as a reference for relevant stakeholders such as investors. Further studies need to be developed by adding non-financial criteria in the firm and developing the criteria to differentiate each other.