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All Journal Format : Jurnal Imiah Teknik Informatika Jurnal CoreIT KOMPUTIKA - Jurnal Sistem Komputer Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) JiTEKH (Jurnal Ilmiah Teknologi Harapan) Jurnal Pendidikan dan Teknologi Indonesia Bulletin of Computer Science Research J-Intech (Journal of Information and Technology) Bulletin of Data Science Paradigma Jurnal Ilmiah Informatika dan Ilmu Komputer Bulletin of Informatics and Data Science International Journal of Informatics and Data Science Jurnal Informatika dan Rekayasa Perangkat Lunak
Wang, Junhai
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RAM-MEREC (Root Assessment Method - Method based on Removal Effects of Criteria): A Synergistic Approach to Weight Derivationand Alternative Ranking in the Selection of the Best Intern Employees Permata, Permata; Wang, Junhai; Setiawansyah, Setiawansyah; Pasaribu, A. Ferico Octaviansyah; Wahyudi, Agung Deni
TIN: Terapan Informatika Nusantara Vol 5 No 11 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i11.7198

Abstract

An effective intern selection process requires an objective and systematic approach to decision-making, especially when it involves multiple assessment criteria. This study proposes a combined approach of RAM-MEREC, which is a combination of Method based on Removal Effects of Criteria (MEREC) and Root Assessment Method (RAM), as a method to improve accuracy and reliability in the best internal selection. MEREC is used to objectively determine the weight of criteria based on the impact of the elimination of each criterion on the overall outcome. Meanwhile, RAM is used to generate alternative rankings by considering the root impact of value changes on each candidate's performance. The results of the application of this method show that RAM-MEREC is able to provide a more representative weighting and a more stable and consistent final ranking. The results of the application of this method show that RAM-MEREC is able to provide a more representative weighting and a more stable and consistent final ranking. The results of the calculation of the total score of all alternatives using the evaluation method that has been determined, obtained that Alternative 10 is the best candidate with the highest score of 1.4378, followed by Alternative 6 with a score of 1.4375 and Alternative 3 with a score of 1.4375. This approach not only improves the quality of decision-making, but also minimizes subjectivity and bias in the selection process.
Combination of Response to Criteria Weighting Method and Multi-Attribute Utility Theory in the Decision Support System for the Best Supplier Selection Ulum, Faruk; Wang, Junhai; Megawaty, Dyah Ayu; Sulistiyawati, Ari; Aryanti, Riska; Sumanto, Sumanto; Setiawansyah, Setiawansyah
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1810

Abstract

Choosing the right supplier is a strategic factor in supporting operational efficiency and a company's competitive advantage. This process requires a decision support system that is able to assess various alternatives objectively and in a structured manner. This study aims to develop a decision support system in the selection of the best supplier by combining the Response to Criteria Weighting (RECA) and Multi-Attribute Utility Theory (MAUT) methods. The RECA method is used to objectively determine the weight of each criterion based on the variation of data between alternatives, so as to reduce subjectivity in the weighting process. Meanwhile, the MAUT method functions to calculate the total utility value of each supplier based on the normalization value and weight that has been obtained. The results of the RECA method show the objective weight of each criterion, which is then used in the MAUT calculation process. The results of the analysis, obtained in the best supplier selection based on the total score of each candidate, it can be seen that PT Global Niaga Mandiri ranks first with the highest score of 0.6512, this shows that this company is the best choice in the supplier selection process. In second place is UD Anugrah Bersama with a score of 0.399, followed by PT Indo Logistik Prima in third place with a score of 0.3451. The combination of the RECA and MAUT methods has been proven to be able to produce accurate, rational, and accountable decisions. This system provides a measurable approach in filtering supplier alternatives efficiently and is relevant to be applied to various other multi-criteria decision-making contexts.
Decision Support System in Determining the Optimal Raw Material Supplier Using a Combination of Entropy and MOORA Wang, Junhai; Ahmad, Imam; Setiawansyah, Setiawansyah
FORMAT Vol 14, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2025.v14.i2.006

Abstract

The selection of the right raw material supplier plays a crucial role in ensuring the efficiency and sustainability of supply chain management. However, the decision-making process is often complex due to the multiple criteria that must be considered simultaneously, such as quality, price, delivery timeliness, production capacity, and flexibility. To address this challenge, this study applies a decision support system that integrates the Entropy method for objective weighting of criteria and the MOORA method for ranking alternatives. Entropy weighting provides an unbiased determination of the importance of each criterion based on data variation, while MOORA delivers a systematic ranking of suppliers by combining benefit and cost criteria into a comprehensive performance score. The results of the analysis on eight supplier alternatives show that Supplier S8 achieves the highest ranking, followed by Supplier S3 and Supplier S6, indicating their superior ability to meet the defined criteria, especially in capacity and flexibility. Meanwhile, Supplier S4 ranks the lowest, reflecting its relatively weaker performance across several aspects. These findings demonstrate that the combination of Entropy and MOORA provides a reliable, objective, and transparent framework to support decision-making in supplier selection.
Decision Support System for Performance Assessment of the Best Salesperson with the Integration of Entropy and WASPAS Wang, Junhai; Setiawansyah; Isnain, Auliya Rahman
International Journal of Informatics and Data Science Vol. 2 No. 2 (2025): June 2025
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v2i2.88

Abstract

The salesperson performance assessment is an important aspect of improving the effectiveness of a company's marketing strategy. However, this assessment process often faces the challenge of subjectivity, especially in determining the weights of the criteria used. To address this issue, this study implements a combination of the Entropy and WASPAS methods. The Entropy method is used to objectively determine the weights of the criteria based on data variation, while the WASPAS method is used to evaluate and rank alternatives. A case study was conducted on five salesperson personnel with the criteria used in selecting the best salesperson being sales target achievement, product mastery, communication skills, creativity, and work ethics. The results showed that Muhammad Iqbal (A3) ranked first with a score of 0.882, followed by Andi Saputra (A1) with a score of 0.796, Rizky Kurniawan (A5) with a score of 0.770, Budi Santoso (A2) with a score of 0.724, and Siti Rahmawati (A4) with a score of 0.655. The main contribution of this research is to present a more accurate and objective salesperson performance evaluation model through the integration of the Entropy–WASPAS method. This finding has practical implications for companies in selecting the best employees, identifying salesperson personnel with outstanding performance, and supporting strategic decision-making in human resource development in the marketing field.
Optimizing E-Commerce Platform Selection Using Root Assessment Method and MEREC Weighting Wang, Junhai; Darwis, Dedi; Gunawan, Rakhmat Dedi; Ariany, Fenty
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Volume 6 Number 1 March 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i1.6

Abstract

The number of users of e-commerce platforms has increased significantly in recent years, and consumers are now more likely to shop online due to ease of access, diverse product choices, and flexibility in transaction times. The difficulty in determining the best e-commerce platform is often caused by subjectivity in the weighting of the criteria used for evaluation. The weighting process is carried out based on the preferences of certain individuals or groups, without considering objective data. This research aims to apply an objective, structured, and accurate approach in evaluating and ranking e-commerce platforms based on relevant multi-dimensional criteria. By using the root assessment method, the evaluation process can be carried out systematically through hierarchical analysis, while the MEREC weighting ensures that the weight of each criterion reflects its real impact on the outcome of the decision. Through the combination of these two methods, this research is expected to make a significant contribution to improving the quality of decision-making, especially in helping users or business people choose the e-commerce platform that best suits their needs. The results of the final score calculation Platform E was ranked first with the highest score of 4.87083, Platform A was ranked second with a score of 4.85162, and Platform B was ranked third with a score of 4.83842. Future research should address the identified limitations by exploring the integration of advanced predictive analytics and artificial intelligence techniques to improve the adaptability and resilience of models. In addition, sensitivity analysis of the MEREC Root Assessment and Weighting Methods should be performed to understand its performance under various data conditions.
Decision Support System Based on RECA and COPRAS Methods in Performance Evaluation of Non-Permanent Employees Asistyasari, Ayuni; Chandra, Iryanto; Hadad, Sitna Hajar; Nuryaman, Yosep; Wang, Junhai
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 3 (2025): Volume 6 Number 3 September 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i3.848

Abstract

The evaluation of the performance of non-permanent employees is a significant challenge for organizations due to the high turnover rate and the limited tenure of these employees. The manual evaluation processes often lead to biases, inconsistencies, and a lack of accuracy in supporting decision-making. This research aims to develop a decision support system based on the RECA and COPRAS methods to produce a more objective, transparent, and systematic evaluation. RECA is used to determine the criteria weights proportionally based on each contribution, while COPRAS functions to assess and provide a final ranking of employee performance by considering both benefit and cost-type criteria. The research results show that this system is capable of sorting non-permanent employees fairly with ranking results of E-AS-05 with a score of 100%, E-AS-03 with a score of 97.32%, E-AS-01 with a score of 94.03%, E-AS-02 with a score of 88.34%, and E-AS-04 with a score of 82.19%. The integration of the RECA and COPRAS methods not only enhances the effectiveness of performance evaluation but also provides a tangible contribution to supporting more efficient and sustainable human resource management.
Optimizing Employee Admission Selection Using G2M Weighting and MOORA Method Rahmanto, Yuri; Wang, Junhai; Setiawansyah, Setiawansyah; Yudhistira, Aditia; Darwis, Dedi; Suryono, Ryan Randy
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 1 (2025): March 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i1.8224

Abstract

An objective and effective employee admission selection process is a crucial step for the success of the organization in achieving its goals. Problems in employee recruitment selection often arise due to a lack of good planning and system implementation, namely decisions are often influenced by personal preferences, stereotypes, or non-relevant factors, thus reducing objectivity in choosing the best candidates. Objective selection ensures that candidate assessments are conducted based on measurable, relevant, and bias-free criteria, so that only individuals who truly meet the company's needs and standards are accepted. The purpose of developing an optimal approach in employee admission selection using G2M weighting and MOORA is to create a more objective, efficient, and accurate selection process. This approach aims to integrate the calculation of criterion weights mathematically, such as those offered by G2M, in order to eliminate subjective bias in determining criterion prioritization. The MOORA method of evaluating alternative candidates is carried out through ratio analysis that takes into account various criteria simultaneously, resulting in a transparent and data-driven ranking. The results of the employee admission selection ranking based on the criteria that have been evaluated, Candidate 3 obtained the highest score of 0.4177, indicating that this candidate best meets the expected criteria. The second position was occupied by Candidate 6 with a score of 0.3886, followed by Candidate 9 with a score of 0.3528. This research contributes to the recruitment process, by providing a more reliable, transparent, and less subjective way of selecting the right candidates for the positions that companies need.
Optimization of Production Operator Performance Assessment with Grey Geometric Mean Weighting and Combinative Distance-based Assessment Wang, Junhai; Setiawansyah, Setiawansyah; Ulum, Faruk; Yudhistira, Aditia; Wahyudi, Agung Deni
Komputika : Jurnal Sistem Komputer Vol. 14 No. 2 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i2.15977

Abstract

The performance of production operators plays a crucial role in determining the level of efficiency and effectiveness of the manufacturing process in a company that has a long-term impact on the company's competitiveness. Production operator performance appraisals often face a number of problems that can reduce the accuracy and fairness of evaluations. One of the main problems is the subjectivity of assessment, where evaluation is based more on the personal perception of the supervisor or assessor without a consistently measurable standard. The purpose of this study is to apply a more objective, structured, and accurate production operator performance evaluation model by integrating the grey geometric mean weighting (G2M Weighting) method as an uncertainty-based criterion weighting approach and combinative distance-based assessment (CODAS) as an alternative ranking method. The results of the production operator's performance ranking are that CR Operator ranks first with the highest performance score of 0.7737, GM Operator is ranked second with a score of 0.6187, followed by AN Operator in third place with a score of 0.5895. This research makes a significant contribution to the development of a performance evaluation system in the manufacturing industry environment by integrating the G2M Weighting and CODAS methods as an objective and systematic approach.
Integration of G2M Weighting and MOORA in Accurate Decision Making for Best Alternative Selection Setiawansyah, Setiawansyah; Wang, Junhai; Palupiningsih, Pritasari
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 2 (2025): December 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i2.36679

Abstract

The goal of the integration of the G2M Weighting and MOORA methods is to produce the best alternative selection decisions that are more accurate and objective. By combining rational criteria weighting through G2M Weighting and alternative evaluation using MOORA, it is hoped that it can reduce bias and increase transparency in decision-making. In addition, this study compares alternative ratings from the application of the MOORA method and other weighting methods. The results of the evaluation and ranking of scholarship recipients using G2M weighting and MOORA, CF candidates managed to occupy the first position with a final score of 0.2727, showing the best performance among all candidates. In second place, UT candidates obtained a score of 0.2630, followed by DF candidates with a score of 0.2445 and SS candidates with a score of 0.2425. This approach makes it a very useful solution in the selection of the best alternatives in a wide range of multi-criteria decision applications. The results of the Spearman correlation test showed that the G2M weighting method had the highest correlation of 0.9879, which showed a very high similarity with the initial rating. The Entropy Weighting and CRITIC methods also showed a strong correlation, of 0.9515 and 0.9636, respectively, although there was slight variation in the alternate sequence. Meanwhile, the MEREC weighting has the lowest correlation of 0.9273, but still shows a very strong relationship. Overall, these results suggest that the G2M method produces rankings consistent with the initial rankings, with variations indicating sensitivity to criterion weighting.
Combination of MOORA and ITARA Methods in Decision Support Systems for Measuring the Performance of Quality Control Teams Hendrastuty, Nirwana; Wang, Junhai; Sulistiyawati, Ari; Darwis, Dedi; Setiawansyah, Setiawansyah; Jumaryadi, Yuwan; Sumanto, Sumanto
TIN: Terapan Informatika Nusantara Vol 6 No 6 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i6.8382

Abstract

The problems that often arise in evaluating the performance of the Quality Control team are the subjectivity in determining the weight of criteria and the limitations of traditional methods in producing objective and consistent rankings. To address this issue, this research integrates the Indifference Threshold-based Attribute Ratio Analysis (ITARA) and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) methods within a decision support system. The ITARA method is used to determine the weights of criteria based on data variation, making them more representative of real conditions, with the result that Accuracy of Product Defect Identification becomes the most dominant criterion with a weight of 0.3999, followed by Response Speed to Issues at 0.1877, while other criteria have lower weights. Furthermore, the MOORA method is used to calculate the preference of alternatives, resulting in a final ranking. The analysis results indicate that the Quality Assurance Team ranks first, followed by the Quality Improvement Team in second place, while the Quality Inspection Team is in the last position. To test the reliability of the model, a sensitivity analysis was conducted by varying the weights of the main criteria. The results show that the ranking structure is relatively stable, with changes only occurring in the positions of the first and second ranks when the accuracy weight is reduced by 0.2. In conclusion, the combination of ITARA-MOORA proves to be capable of producing objective, robust, and reliable performance evaluations as a basis for strategic decision-making in enhancing the quality of the quality control teams.
Co-Authors Aditia Yudhistira Alita, Debby Ari Sulistiyawati Ariany, Fenty Asistyasari, Ayuni Chandra, Iryanto Dedi Darwis Gunawan, Rakhmat Dedi Imam Ahmad Isnain, Auliya Rahman Jumaryadi, Yuwan Megawaty, Dyah Ayu Mesran, Mesran Nirwana Hendrastuty Nuryaman, Yosep Oprasto, Raditya Rimbawan Palupiningsih, Pritasari Pasaribu, A. Ferico Octaviansyah Permata, Permata Riska Aryanti Ryan Randy Suryono Setiawansyah Setiawansyah Sitna Hajar Hadad Sumanto, Sumanto Ulum, Faruk Wahyudi, Agung Deni Yuri Rahmanto