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All Journal Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) Jurnal Informatika dan Rekayasa Perangkat Lunak
Chandra, Iryanto
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Journal : Jurnal Informatika dan Rekayasa Perangkat Lunak

 1 
Integration of RECA Weighting and MARCOS Methods in Decision Support Systems for the Selection of the Best Customer Recommendations Asistyasari, Ayuni; Arshad, Muhammad Waqas; Chandra, Iryanto; Nuryaman, Yosep; Saputra, Very Hendra
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2025): Volume 6 Number 2 June 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i2.219

Abstract

In a competitive business environment, selecting the best customers is a strategic step to improve marketing efficiency and build profitable long-term relationships. However, this process is often constrained by subjectivity in determining criteria and evaluating alternatives. This study aims to develop an objective and measurable decision-making model by integrating of the Respond to Criteria Weighting (RECA weighting) and the method of measurement of alternatives and ranking according to compromise solution (MARCOS). The RECA weighting is used to determine the weight of criteria based on the response to their level of importance, while MARCOS is used to evaluate and rank customer alternatives based on proximity to the ideal solution. The final ranking of customers is determined using the RECA weighting method and MARCOS, which reflects the final value of each customer alternative; Customer 3 obtained the highest final score of 1.2339, indicating the best overall performance based on the established evaluation criteria. Furthermore, Customer 7 and Customer 1 are in second and third place with scores of 1.2096 and 1.1546, respectively, indicating that these three customers are the main candidates to be prioritized in the customer relationship strategy. The result of the integration of these two methods provides a decision support system that is able to generate accurate and logical customer ratings, and supports data-driven strategic decision-making. This model is expected to be an effective solution in improving the quality of business decisions, especially in managing customer relationships more on target and efficiently.
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.
Co-Authors Aditia Yudhistira Arshad, Muhammad Waqas Asistyasari, Ayuni Megawaty, Dyah Ayu Nuryaman, Yosep Setiawansyah Setiawansyah Sitna Hajar Hadad Sufiatul Maryana Very Hendra Saputra Wang, Junhai