Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6, 9219-9229 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i6.3975 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: novipadang06@gmail.com TOPSIS method analysis for company financial performance ranking based on DER, ROA, AND PBV Novi Natalia Padang1*, Erlina2, Iskandar Muda3, Abdillah Arif Nasution4 1,2,3,4Fakultas Ekonomi dan Bisnis Universitas Sumatera Utara, Indonesia; novipadang06@gmail.com (N.N.P.) erlina@usu.ac.id v (E.) iskandar1@usu.ac.id (I.M.) badiqq@yahoo.com (A.A.N.). Abstract: Energy resources are a crucial component of economic and social growth in Indonesia. Energy businesses are a significant part of the national economy, and their performance directly affects the stability of energy supply, economic stability, and the attainment of sustainable development objectives. application of the TOPSIS method (Order Preference Technique with Similarity to Ideal Solution) to initiate and rank the financial performance of energy companies in Indonesia. The main focus of this study is on three financial indicators: Debt to Equity Ratio (DER), Return on Assets (ROA), and Price to Book Value (PBV). The TOPSIS method offers a systematic and objective framework for decision making by normalizing data, assigning weights to each criterion, and calculating the distance from the ideal solution. The results show that this method can effectively rank companies based on their financial metrics, which can facilitate better strategic decision making in the energy sector. This study aims to contribute to the development of information technology-based decision support systems to improve energy resource management in Indonesia and drive sustainable economic growth. In this analysis, the MLBI company achieved the highest ranking with a closeness coefficient of 0.703569, outperforming other companies such as KEJU and AISA. This ranking can be used to guide strategic decisions, improve debt management, and increase stakeholder trust. Keywords: Debt to equity ratio, Price to book value, Return on assets, TOPSIS. 1. Introduction Energy resources are a crucial component of economic and social growth in Indonesia [1]. Energy businesses are a significant part of the national economy, and their performance directly affects the stability of energy supply, economic stability, and the attainment of sustainable development objectives [2]. The Indonesian government aims to enhance the production and management of energy resources in a more efficient, sustainable, and results-oriented manner. In this environment, assessing the performance of energy companies is vital to guarantee that the sector maximally contributes to economic growth, financial stability, and societal welfare [3]. The assessment of energy firm performance not only ascertains their ability to achieve corporate objectives but also initiates the formulation of strategic policy suggestions. This assessment encompasses multiple financial metrics that indicate operational efficiency, financial stability, and corporate competitiveness. The primary indicators evaluated in these assessments include the Debt-to- Equity Ratio (DER), Return on Assets (ROA), and Price to Book Value (PBV). DERs indicates the proportion of debt to equity in a company's capital structure, where a lower DERs value signifies effective debt management and diminished risk levels. Return on Assets (ROA) is a crucial metric that reflects a company's capacity to create profit via the effective utilization of its assets; elevated ROA values signify superior operational efficiency [4]. PBV denotes the ratio of a company's market value to its book value, hence indicating the market's evaluation of the company's performance[5]. Evaluating the success of energy companies using these measures is challenging, especially when conducted manually. Intricate data analytics, assessment of various alternatives, and the potential for 9220 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9219-9229, 2024 DOI: 10.55214/25768484.v8i6.3976 © 2024 by the authors; licensee Learning Gate bias can adversely affect the precision of evaluation outcomes. This case underscores the significance of methodical, trustworthy, and impartial decision-making processes. Decision Support Systems (DSS) are, however, crucial to this issue. A Decision Support System (DSS) is an information technology-based framework that equips decision-makers with analytical tools to evaluate data and formulate suggestions through organized mathematical models. One of the most prevalent methods for addressing multi- criteria Decision-Making issues in Decision Support Systems (DSS) is the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [6]. The ACE (A⁺), representing the optimal positive values of each criterion, and the ACE (A⁻), denoting the optimal negative values of each criterion. An optimal solution is one that is as near as possible to the superior ideal solution and as distant as possible from the inferior ideal solution. In the evaluation of energy firm performance, TOPSIS is a methodology that integrates several criteria, such as DERs, ROA, and PBV, into a systematic assessment framework. This technique facilitates decision- making while enhancing the complexity, transparency, and reliability of the measurement outcomes. In previous research, the AHP [7] VIKOR [7], [8] CIMAS-CRITIC-RBNAR [9] method was also used in company assessment. Debt-to-Equity Ratio, Return on Assets, and Price-to-Book Value. We chose the method for its capacity to efficiently manage multicriteria data in an objective manner, generating appropriate decision suggestions for strategic objectives. Energy firms can enhance the efficiency of performance assessments, reduce subjectivity, and achieve higher objectivity and results by implementing the TOPSIS technique [10]. Furthermore, we anticipate that the findings of this study will aid in the advancement of IT-based decision-making systems, a critical consideration in the digital era. The application of the TOPSIS technique offers immediate practical benefits for evaluating corporate performance and also has strategic implications for Indonesia's energy sector. Enhanced evaluation outcomes can serve as a basis for energy organizations to refine their operational strategies, enhance financial management, and bolster market competitiveness. Conversely, the results of this assessment can inform the development of more effective policy recommendations for sustainable energy management. This research will elucidate the implementation of the TOPSIS technique, encompassing the collecting of valid data, the calculation of criteria weights, and the consequent preference score. This study aims to elucidate the application of the TOPSIS technique in IT-based decision-making systems, particularly addressing the suitability and reliability of the criteria employed. We anticipate that this effort will contribute to the development of a more efficient decision support system to assist in the administration of Indonesia's energy sector, expedite digitalization, and so strengthen measures for sustainable economic growth. Finally, the preference score (Ci) value for each alternative is calculated using the following formula. 2. Method The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) technique provides a systematic and impartial framework for assessing and ranking corporate performance across several parameters. The procedure commences with the formulation of a decision matrix that encompasses evaluation criteria for appraising each company's alternatives. The matrix comprises rows that denote companies (alternatives) and columns that signify the criteria employed in the evaluation. This study utilizes financial records of companies listed on the Indonesia Stock Exchange, employing ROA (Return on Assets) as a performance metric with higher values indicating more returns, and DAR (Debt to Asset Ratio) as a risk metric with lower values signifying reduced liabilities. This matrix is the basis for ensuing calculations. It is now essential to normalize the choice matrix to ensure that all criteria values are on a uniform scale, facilitating a fair comparison of the options. Normalization standardizes the values of each criterion, regardless of their original scales or units, to a uniform scale. This is accomplished by use the formula 9221 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9219-9229, 2024 DOI: 10.55214/25768484.v8i6.3976 © 2024 by the authors; licensee Learning Gate 𝑅𝑖𝑗 = 𝑋𝑖𝑗 √∑ 𝑋𝑖𝑗 2 (1) Here, Rij represents the normalized version of the j-th criterion for the i-th choice, while Xij signifies the original version. This phase ensures that all criteria are assessed on an equivalent basis. Weights are normalized and allocated to each criterion according to their respective significance in the evaluation process. Such weights are determined by practical competence, stakeholder perspectives, or strategic objectives. The normalized matrix is further adjusted by the weights of each criterion to derive the weighted decision matrix according to the formula. 𝑉𝑖𝑗 = 𝑊𝑗. 𝑅𝑖𝑗 (2) In which Wj represents the weight of the j-th criterion. This phase emphasizes the significance of each criterion in the evaluation of a company's performance. Subsequently, the ideal solutions are derived in this order: positive (A⁺) and negative (A⁻). The optimal state is the good ideal solution, which involves optimizing the benefit criteria and minimizing the cost criteria. Conversely, the poor ideal solution is the optimal state, which involves maximizing the cost criteria and minimizing the benefit criteria. By selecting the maximum and minimum values for each criterion in the weighted decision matrix, these optimal solutions are achieved. Subsequently, the Euclidean distance formula is employed to determine the value of each alternative from the positive and negative ideal solutions. The distance to the positive ideal solution is determined as 𝐷𝑖 + = √∑(𝑉𝑖𝑗 − 𝐴𝑗 +)2 (3) while the distance to the negative ideal solution is 𝐷𝑖 − = √∑(𝑉𝑖𝑗 − 𝐴𝑗 −)2 (4) where Vij is the weighted normalized value, and the positive and negative ideal solutions of the j -th criterion are A+j , and A-j , respectively. Those distances measure the closeness of each alternative to the optimal solution and the distance of an alternative from the worst-case solution. Finally, the preference score (Ci) value for each alternative is calculated using the following formula. 𝐶𝑖 = 𝐷𝑖 − 𝐷𝑖 ++𝐷𝑖 − (5) The preference score ranges from 0 to 1, with elevated preference linked to alternatives nearer to the optimal solution ideal. Alternatives are subsequently rated based on their preference scores, with the greatest score signifying superior firm performance. The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) technique provides an easy, data-driven framework for ranking corporate performance by comprehensively including several criteria within a holistic evaluation system. This methodology is particularly advantageous for assessing financial performance and decision-support systems, where clarity and objectivity are prioritized. 3. Results and Discussion 3.1. Observation Findings The system that is currently employed to calculate company performance, but particularly for the energy and mining industries, remains manual currently. Company performance data are gathered from 9222 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9219-9229, 2024 DOI: 10.55214/25768484.v8i6.3976 © 2024 by the authors; licensee Learning Gate different sources, which are not joined, and thus inefficiency, error, and lack of standardization occur. In the evaluation of 27 companies in 2023, the analysis focused on important metrics such as Debt to Equity Ratio (DER), Return on Assets (ROA), and Price to Book Value (PBV). These metrics give us a picture of financial stability, profitability, and market valuation, although traditional manual techniques are often labor-intensive and error prone. To solve this issue, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method provides a better alternative. TOPSIS is used for multi-criteria decision making by evaluating companies in terms of their distance from the optimal solution. The process consists in the data normalization to standardize the scale, assigning weights to each criterion and the calculation of the distance between the positive and negative solutions. The final output is a ranking of companies according to preference scores, thus revealing companies' positions in comparison. The TOPSIS method is applied to make the evaluation process more accurate, efficient and transparent. In this technique, analysis time is less while the risk is reduced, and timely and data-driven decision making is facilitated. Using this facility, actors can more clearly see the financial performance of the company and therefore more effectively make strategic decisions in the energy and mining business. 3.2. Discussion The observation results above necessitate the use of an appropriate method to accurately calculate the company's performance as shown in the table. The steps taken in this company performance assessment include a review of all data used in the decision-making process. The financial ratios in the company performance assessment that are criticized include DER, ROA, and PBV. The data that is categorized are the values of DER, ROA, and PBV. The use of data is identified based on aspects of the company's liquidity ratio, profitability, and valuation. Meanwhile, the determination of criteria, cost-benefit, and attribute values in this assessment has a role as a measuring tool for calculations that can guarantee evaluation in accordance with the established criteria so as to support more effective and objective decisions. Table 1: Company DER, ROA, dan PBV data. Company DER ROA PBV ADES 0.069 0.222 1.229 AISA 0.028 0.274 1.700 BUDI 0.712 0.023 0.337 CAMP 0.130 0.041 1.848 CEKA 0.243 0.116 0.009 CLEO 0.465 0.101 6.706 COCO 0.800 0.010 0.013 DLTA 0.202 0.101 3.454 DMND 0.220 0.036 1.871 ENZO 0.835 0.004 0.732 GOOD 0.787 0.037 3.234 HOKI 0.369 0.042 3.666 ICBP 0.741 0.072 2.219 IKAN 0.772 0.008 1.772 INDF 0.809 0.054 0.760 KEJU 0.531 0.179 4.610 MLBI 0.819 0.233 1.374 MYOR 0.755 0.106 5.376 PANI 0.001 0.002 0.001 PMMP 0.003 0.041 0.009 PSDN 0.799 0.115 1.558 9223 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9219-9229, 2024 DOI: 10.55214/25768484.v8i6.3976 © 2024 by the authors; licensee Learning Gate Company DER ROA PBV ROTI 0.379 0.038 0.448 SKBM 0.735 0.003 0.581 SKLT 0.779 0.055 2.656 STTP 0.290 0.182 4.655 TBLA 0.002 0.035 0.848 ULTJ 0.057 0.127 3.479 The table below shows financial performance information from different companies based on the three key metrics—Debt to Equity Ratio (DER), Return on Assets (ROA), and Price to Book Value (PBV). DER reflects the level to which a company finances its activities through debt, hence higher values are measured by the higher reliance on debt. ROA indicates the effectiveness of a company in using assets to generate profits, and higher values indicate a more efficient use of owned assets. In the meantime, PBV delivers a summary of the transaction value of the company vs. its book value, where higher values reflect that the market is giving the company a higher valuation. We are able to perform comprehensive analysis of a company's performance to evaluate its financials in this data analysis considering aspects of liquidity, profitability and market valuation, all of which are important for investment decisions and firms in the capital market. Table 2: Normalization result data. Company DER ROA PBV ADES 0.00560 0.09836 0.02229 AISA 0.00227 0.12140 0.03083 BUDI 0.05774 0.01019 0.00611 CAMP 0.01054 0.01817 0.03351 CEKA 0.01970 0.05140 0.00016 CLEO 0.03771 0.04475 0.12161 COCO 0.06487 0.00443 0.00024 DLTA 0.01638 0.04475 0.06263 DMND 0.01784 0.01595 0.03393 ENZO 0.06771 0.00177 0.01327 GOOD 0.06382 0.01639 0.05865 HOKI 0.02992 0.01861 0.06648 ICBP 0.06009 0.03190 0.04024 IKAN 0.06260 0.00354 0.03213 INDF 0.06560 0.02393 0.01378 KEJU 0.04306 0.07931 0.08360 MLBI 0.06641 0.10323 0.02492 MYOR 0.06122 0.04696 0.09749 PANI 0.00008 0.00089 0.00002 PMMP 0.00024 0.01817 0.00016 PSDN 0.06479 0.05095 0.02825 ROTI 0.03073 0.01684 0.00812 SKBM 0.05960 0.00133 0.01054 SKLT 0.06317 0.02437 0.04816 STTP 0.02352 0.08064 0.08441 TBLA 0.00016 0.01551 0.01538 ULTJ 0.00462 0.05627 0.06309 9224 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9219-9229, 2024 DOI: 10.55214/25768484.v8i6.3976 © 2024 by the authors; licensee Learning Gate In the normalization process of the TOPSIS method, the normalized value of the data from all companies of the DER, ROA, and PBV indicators is transformed into a comparable value format for direct comparison. The goal of this normalization process is to reduce any bias that could occur as a result of the unit or scales differently between the criteria to be validated. Table 2 presents the normalization results of the data reported in Table 1. In this table, the DEA-specific normalization equation normalizes each cell of the DER, ROA, and PBV columns, making for easier analysis and comparison. We decreased the DER value from 0.069 to 0.00560, the ROA value from 0.222 to 0.09836, and the PBV to 0.02229. This operation ensures that all the values are in the same scale, which makes the evaluation in the following stages of the TOPSIS method much easier. This consists of determining the distance between the positive and negative ideal solutions, which plays an important role in the final ranking of each company. Table 3: Terms of criteria and determination of Weights. Code Criteria Range % Weight C1 DER 40 0.4 C2 ROA 40 0.4 C3 PBV 20 0.2 Table 3 reports the estimation of weights for each criterion applied in the TOPSIS technique. Here, we assign a weight to each criterion (i.e., DER, ROA, and PBV) according to their relative weight in the process of decision making. The weight of 40% is given to the DER (C1) and ROA (C2) criteria by the priority analysis according to which their importance in assessing the company performance is emphasis because they are directly related to financial risk and profitability. The PBV (C3) criterion although highly relevant, has a weight of only 20% because of the assumption that it has less impact on company valuation than the DER and ROA criteria. In this step, by applying the above weighting to determine the final score of each alternative in the TOPSIS method, the optimal ranking of all al alternative considering the company's overall performance will be attained. Table 4: Normalization weighting result. Company DER ROA PBV ADES 0.00224 0.03934 0.00446 AISA 0.00091 0.04856 0.00617 BUDI 0.02309 0.00408 0.00122 CAMP 0.00422 0.00727 0.00670 CEKA 0.00788 0.02056 0.00003 CLEO 0.01508 0.01790 0.02432 COCO 0.02595 0.00177 0.00005 DLTA 0.00655 0.01790 0.01253 DMND 0.00714 0.00638 0.00679 ENZO 0.02708 0.00071 0.00265 GOOD 0.02553 0.00656 0.01173 HOKI 0.01197 0.00744 0.01330 ICBP 0.02404 0.01276 0.00805 IKAN 0.02504 0.00142 0.00643 INDF 0.02624 0.00957 0.00276 KEJU 0.01722 0.03172 0.01672 MLBI 0.02657 0.04129 0.00498 MYOR 0.02449 0.01879 0.01950 PANI 0.00003 0.00035 0.000004 9225 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9219-9229, 2024 DOI: 10.55214/25768484.v8i6.3976 © 2024 by the authors; licensee Learning Gate Company DER ROA PBV PMMP 0.00010 0.00727 0.00003 PSDN 0.02592 0.02038 0.00565 ROTI 0.01229 0.00673 0.00162 SKBM 0.02384 0.00053 0.00211 SKLT 0.02527 0.00975 0.00963 STTP 0.00941 0.03226 0.01688 TBLA 0.00006 0.00620 0.00308 ULTJ 0.00185 0.02251 0.01262 Results of weighted normalization on the TOPSIS approach are presented in Table 4. The weighted factors for these criteria are applied to the normalized values of each criterion (DER, ROA, PBV) in this step. In this process, emphasis is also directed to criteria judged to be more significant, based on the weights indicated in Table 3. For example, the ADES company (multiplied the normalized DER value (0.00560) by a weight of 0.4) resulting in a value of 0.00224. In an analogous fashion, it scales the ROA and PBV values by their weights (resulting in 0.03934 and 0.00446, respectively).The ted normalization procedure generates a final value per company on each criterion and thereby provides an objectively fairer and more comparative evaluation between companies. In the following step, we will apply the outcome of this weighted normalization to calculate the distance between each alternative to the positive and negative ideal solution, which is the mathematical heart of the TOPSIS approach, to rank the companies. Table 5: Solusi Ideal Positif (A+) dan Negatif (A-). A + 0.02708 0.04856 0.02432 A - 0.00003 0.00035 0.000004 Table 5 presents the positive ideal solution (A+ and negative ideal solution (A- of the TOPSIS method. At this point, the upper-limit ideal solution (A+ and the lower-limit ideal solution (A- respectively represents the maximum and minimum possible values for each criterion. We calculate the positive ideal solution (A+ for each criterion, in the basis of the maximum value of the weighted normalization result, i.e., the optimal condition of each criterion. For example, the ROA criterion produces the highest value of 0.04856, an element of the positive idealised solution. As a counterpoint, the bad ideal solution (A− represents the highest values obtained on each criterion. The optimal negative ideal solution for the DER criterion is with a lowest value of 0.00003. Using these positive and negative ideal solutions, the next step in the TOPSIS method is to calculate the distance of each alternative from the positive and negative ideal solutions, which will allow the ranking of companies based on their proximity to the positive ideal solution and the extent to which they avoid the negative ideal solution. Table 6: Menghitung Jarak Euclidean ke Solusi Ideal Positif (A+) dan Negatif (A-). Company A + A - ADES 0.03311 0.03931 AISA 0.03185 0.04861 BUDI 0.05028 0.02340 CAMP 0.05038 0.01050 CEKA 0.04174 0.02168 CLEO 0.03292 0.03355 COCO 0.05272 0.02596 DLTA 0.03874 0.02252 DMND 0.04984 0.01153 9226 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9219-9229, 2024 DOI: 10.55214/25768484.v8i6.3976 © 2024 by the authors; licensee Learning Gate ENZO 0.05253 0.02719 GOOD 0.04388 0.02874 HOKI 0.04517 0.01922 ICBP 0.03944 0.02820 IKAN 0.05046 0.02584 INDF 0.04456 0.02792 KEJU 0.02094 0.03949 MLBI 0.02066 0.04904 MYOR 0.03027 0.03631 PANI 0.06039 0.00001 PMMP 0.05498 0.00692 PSDN 0.03382 0.03321 ROTI 0.04983 0.01392 SKBM 0.05302 0.02390 SKLT 0.04154 0.02860 STTP 0.02517 0.03729 TBLA 0.05455 0.00661 ULTJ 0.03811 0.02556 Table 6 reports the calculation of the Euclidean distance of each company to the positive ideal solution (A+ and the negative ideal solution (A- in the TOPSIS approach. As of this point in time, we compute the Euclidean distance in order to determine the euclidean relative distance between each alternative (company) and the positive and negative optima. A mathematical formula determines this distance, which provides a measure of how far the company is from the optimal solution (A+ and how much freedom it has from the least optimal (A-. For instance, in the ADES company, the distance between the positive ideal solution (A+ and the negative ideal solution (A- is, respectively, 0.03311 and 0.03931. The companies' position based on the evaluated criteria is better when the distance to the positive ideal solution is reduced. On the other hand, the more distant the system is from the negative ideal solution, the more efficient, as the company is then as far away as possible from the worst scenario. In the next step the Euclidean distance is exploited to compute the preference value of each company, and hence its final position is computed in terms of its distance from the negative ideal and distance from the positive ideal solution. Table 7: Menghitung closeness coefficient (C). Company Closeness coefficient ADES 0.542770 AISA 0.604137 BUDI 0.317540 CAMP 0.172454 CEKA 0.341829 CLEO 0.504745 COCO 0.329919 DLTA 0.367683 DMND 0.187814 ENZO 0.341047 GOOD 0.395795 HOKI 0.298521 ICBP 0.416858 IKAN 0.338677 INDF 0.385201 9227 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9219-9229, 2024 DOI: 10.55214/25768484.v8i6.3976 © 2024 by the authors; licensee Learning Gate KEJU 0.653499 MLBI 0.703569 MYOR 0.545308 PANI 0.000084 PMMP 0.111740 PSDN 0.495469 ROTI 0.218356 SKBM 0.310765 SKLT 0.407767 STTP 0.597043 TBLA 0.108084 ULTJ 0.401465 Table 7 illustrates the computed Closeness Coefficient (C) values for each company that participated in the TOPSIS approach. Each alternative (company) estimates its closeness coefficient toward the positive ideal solution (A+ and its distance to the negative ideal solution (A- as a size measure. We determine the Closeness Coefficient value by means of the Euclidean distance between previous stage and a formula that includes the company position relative to the positive ideal solution and the company position relative to the negative ideal solution. The higher the value of the Closeness Coefficient, the higher the corresponding performance of the company regarding the comparison of the used criteria. For example, the MLBI company has the maximum closeness coefficient of 0.703569, meaning it is nearest to the positive ideal solution and furthest from the negative ideal solution. On the other hand, PANI has an extremely low Closeness Coefficient value (0.000084), so that, this company is very distant from the positive ideal information solution and also very close to the negative one. We employ this Closeness Coefficient value to order the companies in terms of their performance from best to worst, giving the highest rating to the company with the greatest C value as the better alternative in the context of making decisions. Table 8: Hasil Perangkingan. Company Closeness coefficient Rank MLBI 0.703569 1 KEJU 0.653499 2 AISA 0.604137 3 STTP 0.597043 4 MYOR 0.545308 5 ADES 0.54277 6 CLEO 0.504745 7 PSDN 0.495469 8 ICBP 0.416858 9 SKLT 0.407767 10 ULTJ 0.401465 11 GOOD 0.395795 12 INDF 0.385201 13 DLTA 0.367683 14 CEKA 0.341829 15 ENZO 0.341047 16 IKAN 0.338677 17 COCO 0.329919 18 BUDI 0.31754 19 SKBM 0.310765 20 9228 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9219-9229, 2024 DOI: 10.55214/25768484.v8i6.3976 © 2024 by the authors; licensee Learning Gate HOKI 0.298521 21 ROTI 0.218356 22 DMND 0.187814 23 CAMP 0.172454 24 PMMP 0.11174 25 TBLA 0.108084 26 PANI 0.000084 27 Table 8 presents the ranking results using the Closeness Coefficient (C) value obtained in the corresponding stage in the TOPSIS approaches. Currently, we sort companies from the highest to the lowest Closeness Coefficient value. This company with the greatest C value is preferred as the optimal alternative, as it is more similar to the positive ideal solution (A+ and less similar to the negative ideal solution (A-. For example, MLBI achieves the highest ranking with a closeness coefficient of 0.703569, highlighting its better performance than other candidate methods. After KEJU obtained the second rank with a closeness coefficient (0.653499), AISA obtained the third rank with a closeness coefficient (0.604137), and among the rest, the last rank with an extremely low Closeness Coefficient value (0.000084), that is indicating the least successful of companies. The rank results of TOPSIS analysis- based ranking analysis clearly present the ranking performance of the company, and are used as references for subsequent decision making. 4. Conclusion MLBI ranks highest (closing coefficient (C) of 0.703569) with the aid of results from the TOPSIS approach. This success demonstrates the superior performance of MLBI with respect to the three main financial indicators considered—Debt to Equity Ratio (DER), Return on Assets (ROA), and Price to Book Value (PBV). These findings highlight the degree to which, on the one hand, the company is living very close to the positive ideal solution (A+, the most optimal scenario, and, on the other hand, to the much greater distance from the negative ideal solution (A-, the worst-case scenario. The weight of the ranking goes far beyond numbers as it is an important driver of the strategic choice of the company and contributes to the bigger picture of economic development. Based on these findings, the business can pinpoint specific areas that need to be changed, improve its debt management, improve investment decisions and better direct resources. 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