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All Journal Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) KLIK: Kajian Ilmiah Informatika dan Komputer BEES: Bulletin of Electrical and Electronics Engineering Paradigma Bulletin of Informatics and Data Science Jurnal Informatika dan Rekayasa Perangkat Lunak
Arshad, Muhammad Waqas
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Comparative Analysis of the Combination of MOORA and GRA with PIPRECIA Weighting in the Selection of Warehouse Heads Arshad, Muhammad Waqas; Setiawansyah, Setiawansyah; Sintaro, Sanriomi
BEES: Bulletin of Electrical and Electronics Engineering Vol 4 No 3 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bees.v4i3.4922

Abstract

The Head of Warehouse has the main responsibility to manage and supervise the daily operations of the warehouse, including receiving, storing, and dispensing goods. In addition, the warehouse head is responsible for leading the warehouse staff team, providing the necessary direction, training, and supervision to ensure efficiency and safety in warehouse operations. This study aims to select warehouse heads by applying a combination of MOORA and PIPRECIA methods, as well as GRA and PIPRECIA. The combination of MOORA and PIPRECIA as well as GRA and PIPRECIA will be compared based on the final ranking of the two methods used. Based on the ranking results on the MOORA Method, the highest value with a value of 0.40017 was obtained by FHY with rank 1, a value of 0.36637 was obtained by AGL with rank 2, and a value of 0.35721 was obtained by YLS with rank 3 Based on the rating results on the GRA Method, the highest value with a value of 0.12164 was obtained by AGL with rank 1, a value of 0.12017 was obtained by FHY with rank 2, and a value of 0.1054 obtained by TAN with rank 3. Ranking differences between Multi-Method Objective Optimization by Ratio Analysis (MOORA) and Gray Relational Analysis (GRA) can arise due to differences in approaches in evaluating and comparing alternatives. The results of the conformity test can be concluded in the selection of warehouse heads who recommend the GRA method compared to the MOORA method, because the results of the conformity level of the GRA method get a value of 99.99919% higher than the MOORA method.
Modification of Multi-Attribute Utility Theory in Determining Scholarship Recipient Students Arshad, Muhammad Waqas; Setiawansyah, Setiawansyah; Rahmanto, Yuri; Palupiningsih, Pritasari; Maryana, Sufiatul
BEES: Bulletin of Electrical and Electronics Engineering Vol 5 No 1 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bees.v5i1.5523

Abstract

Educational scholarships are financial aid given to students or students to support the financing of their education. Mistakes in the assessment of scholarship recipients are often related to subjectivity and lack of transparency in the selection process. Unclear or inconsistent assessment criteria can lead to unfairness, where some deserving candidates may not get the same opportunities. The number of data used in Determining Scholarship Recipient Students is 10 students. The purpose of the MAUT modification research with geometric mean in producing criterion weights is to improve accuracy, stability, and consistency in the decision-making process. This study also aims to test the effectiveness of the geometric mean method in producing more objective and structured weights, as well as compare it with other traditional MAUT methods such as direct addition or multiplication. The modification of the MAUT method with a geometric mean is named G-MAUT. The results of the ranking of scholarship recipients using the G-MAUT method the first-place scholarship recipient with a final score of 1.0048 was obtained by Student 3, the second-place scholarship recipient with a final score of 0.6260 was obtained by Student 8, and the third-place scholarship recipient with a final score of 0.5048 was obtained by Student 5. This modification under the name G-MAUT allows for a more holistic and comprehensive assessment of potential recipients, ensuring that non-academic aspects are also taken into account proportionately.
Modification of the Grey Relational Analysis Method in Determining the Best Mechanic Arshad, Muhammad Waqas; Sulistiani, Heni; Maryana, Sufiatul; Palupiningsih, Pritasari; Rahmanto, Yuri; Setiawansyah, Setiawansyah
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5678

Abstract

Determining the best mechanics in the industry has an important role to ensure the quality and reliability of the products and services offered. Competent and experienced mechanics are able to diagnose and repair accurately and efficiently, thereby minimizing operational downtime and increasing productivity. Without a structured system, mechanical performance appraisals tend to be subjective and inconsistent, which can lead to dissatisfaction among employees and customers. Mechanics may not get clear and constructive feedback on their performance, thus hindering skill development and professionalism. The purpose of the research of the modified Grey Relational Analysis (GRA) using standard deviation is to improve the accuracy and reliability of the decision-making process in situations where the data has a high degree of variability or significant uncertainty. By integrating standard deviations into the GRA, the study aims to account for variations and fluctuations in the data, which allows for more accurate and representative assessment of the criteria. This modification is expected to overcome the weaknesses of traditional GRAs that may not adequately consider data uncertainty, as well as produce more robust and realistic alternative rankings. The results of the best ranking of mechanics, Mechanic FR ranks first with a value of 0.11, followed by Mechanic HS with a value of 0.104. The third place was occupied by Mechanic AY with a score of 0.099.
Selection of the Best Customer using a Combination of Rank Order Centroid and Grey Relational Analysis Arshad, Muhammad Waqas; Rahmanto, Yuri; Setiawansyah, Setiawansyah
Bulletin of Informatics and Data Science Vol 3, No 1 (2024): May 2024
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v3i1.84

Abstract

A customer is an individual or entity that purchases goods or services from a company or organization. They play an important role in business success, as customer satisfaction and loyalty can determine a company's reputation and sustainability in the marketplace. One of the main challenges is collecting and analyzing accurate and comprehensive data regarding purchase behavior, transaction frequency. Other challenges include keeping customer data confidential and ensuring that the selection process is fair and transparent. The ROC method is used in the initial stage to determine the importance weight of each criterion based on the subjective ranking of the decision makers, which is then converted into numerical weights systematically and consistently, the GRA method is applied to calculate the relational proximity between each customer's alternative to the ideal solution based on their performance values on each criterion.The purpose of this study is to develop and implement a comprehensive framework for the selection of the best customers by combining ROC weighting and GRA methods, and provide practical recommendations for companies in managing and utilizing the best customer relationships, in order to improve customer loyalty and long-term profitability. By combining these approaches, businesses can effectively prioritize customers based on their significance and potential to build long-term relationships and maximize profitability, thus enabling more targeted marketing strategies and better resource allocation. The best customer ranking results were obtained by Customer I with a final GRG value of 0.1792 for the 1st rank, Customer D with a final GRG value of 0.1683 for the 2nd rank, and Customer K with a final GRG value of 0.1505 for the 3rd rank
Modification of Additive Ratio Assessment Method through Distance-Based Weighting Approach for Optimizing Assessment Accuracy Gunawan, Rakhmat Dedi; Arshad, Muhammad Waqas; Wahyudi, Agung Deni; Suryono, Ryan Randy; Widodo, Tri; Ulum, Faruk
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 2 (2025): September 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i2.8810

Abstract

The Additive Ratio Assessment (ARAS) method is one of the approaches in multi-criteria decision making (MCDM) used to determine the best alternative based on a number of predetermined criteria. The drawback of this method is its heavy reliance on the accuracy of the criterion weighting determination; non-objective weights can lead to biased results. This study aims to improve the accuracy of ranking in multicriteria decision-making through the modification of the ARAS method with a distance-based weighting approach called ARAS-D. The ARAS method, known for its simplicity in calculation, was modified to be more responsive to the distribution of alternative data on each criterion. This distance-based weighting approach objectively determines the weight of the criteria based on variations in data performance, thereby reducing subjectivity in the weighting process. A case study was conducted on the selection of a new store location with six main criteria: rental cost, building area, accessibility, consumer traffic, parking availability, and infrastructure. The results of the evaluation show that the ARAS-D method is able to produce more precise ratings than the standard approach. Store locations with the highest utility value are recommended as the best choice, proving the effectiveness of the method in supporting strategic decisions. The results of the New Store Location 5 alternative rating obtained the highest score with a value of 0.9083, indicating that this location is the most optimal choice overall. This is followed by New Store Location 3 with a value of 0.8617 and New Store Location 1 with a value of 0.8415, which also shows excellent performance against the criteria that have been set. This research contributes to the development of more adaptive and data-based decision-making methods.
Comparison of Certainty Factor, Dempster Shafer, and Bayes' Theorem in Expert Systems for Diagnosing Female Reproductive System Diseases Mesran, Mesran; Rasli, Roznim Mohamad; Setiawansyah, Setiawansyah; Arshad, Muhammad Waqas
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8334

Abstract

Expert systems are one application of artificial intelligence used to mimic the ability of an expert in diagnosing a disease. This study aims to compare the performance of three inference methods Certainty Factor, Dempster-Shafer, and Bayes' Theorem in the diagnosis of female reproductive system diseases. Symptom data and expert knowledge values were obtained from medical experts to support the system's validity. Each method was implemented on the same symptom data, and the results were analyzed to assess the consistency of the diagnoses produced. The results show that the Certainty Factor method produced a diagnosis of Cervical Cancer with the highest confidence value of 0.9999, followed by the Dempster-Shafer method with the same diagnosis and a confidence value of 0.852. However, the Bayes Theorem method produced a different diagnosis, namely Ovarian Cyst, with a confidence value of 0.911. These differing results indicate that the characteristics and approaches of each method significantly influence the final diagnosis outcome. This study contributes insights to expert system developers regarding the strengths and weaknesses of each inference method. The selection of the appropriate method must be tailored to the system's requirements, data complexity, and the level of uncertainty in the medical information used.
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.
Combination of Multi-Attributive Ideal-Real Comparative Analysis and Rank Order Centroid in Supplier Performance Evaluation Arshad, Muhammad Waqas; Setiawansyah; Mesran; Desyanti
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1677

Abstract

Supplier performance evaluation is a critical aspect of supply chain management that focuses on assessing and monitoring the performance of suppliers. Supplier performance evaluation not only provides benefits for the company, but also motivates suppliers to improve their quality standards and operational efficiency. This study aims to evaluate supplier performance based on existing assessment data by applying the ROC method to determine the weight of the criteria used, then the MAIRCA method will evaluate supplier performance so that it will produce a rating of supplier performance evaluation which will be a decision recommendation for companies in assessing the performance of existing suppliers. The combination of ROC and MAIRCA weighting methods forms a powerful approach in addressing the complexity and challenges of multi-criteria decision making. ROC with its focus on relative ranking criteria, whereas MAIRCA which considers the difference between ideal and real conditions, presents complementary perspectives. By combining the two, decision makers can generate a more contextual and informational weight of criteria. The ranking result graph in figure 4 shows the best supplier performance obtained on behalf of Supplier C with a final value of 0.052391944 ranked 1, then on behalf of Supplier F with a final value of 0.050077222 ranked 2, and on behalf of Supplier G with a final value of 0.049074028 ranked 3.
Combination of Rank Sum and Multi Attribute Utility Theory in Determining the Best Receptionist Performance Arshad, Muhammad Waqas; Setiawansyah, Setiawansyah; Mesran
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1791

Abstract

As the frontline of the service industry, a receptionist's performance not only reflects his professionalism, but also affects the first impression and overall customer experience. Problems in assessing the performance of a receptionist can include several things, namely difficulty measuring the quality of social interactions objectively, performance appraisals often focus more on administrative tasks, lack of understanding of the receptionist's role as a liaison between the company and customers, difficulty in assessing intangible aspects such as friendliness, patience, or the ability to respond to changing customer needs quickly and efficiently. The combination of rank sum weighting methods and Multi-Attribute Utility Theory (MAUT) can result in a more holistic and robust approach to decision making. In this approach, the rank sum weighting method can be used to get an initial picture of the relative preferences of various alternatives, while MAUT can be used to dig deeper into those preferences and account for complex factors such as attribute weights and utility values. By combining these two methods, decision makers can gain a deeper understanding of their preferences, while still maintaining openness to multiple perspectives and information. The results of the ranking of the best cashier performance the 1st best cashier with a value of 0.574 obtained by Zulaikah, the 2nd best cashier with a value of 0.473 obtained by Arini, and the 3rd best cashier with a value of 0.337 obtained by Lilik Karlina.
Combination of Objective Weighting Method using MEREC and A New Additive Ratio Assessment in Coffee Barista Admissions Arshad, Muhammad Waqas; Suryono, Ryan Randy; Rahmanto, Yuri; Sumanto, Sumanto; Sintaro, Sanriomi; Setiawansyah, Setiawansyah
TIN: Terapan Informatika Nusantara Vol 5 No 3 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i3.5771

Abstract

A coffee barista is a professional who is skilled in the art of brewing and serving coffee in an attractive and high-quality way. The role of a barista is not only limited to operating an espresso machine and grinding coffee beans, but also includes in-depth knowledge of different types of coffee beans, manufacturing techniques, and the resulting flavors. The main problem in the acceptance of coffee baristas often has to do with the gap between industry expectations and the skills possessed by prospective workers. Many candidates may lack formal training or practical experience in brewing coffee, so they do not meet the standards expected by cafes or restaurants. The purpose of the research on the Combination of Objective Weighting Methods using MEREC and ARAS in Coffee Barista Admission is to develop and apply a more systematic and objective approach in the selection process of prospective baristas. The combination of objective weighting methods and the new additive ratio assessment (ARAS) approach offers a sophisticated framework for evaluating candidates in coffee barista admissions. The objective weighting method ensures that evaluation criteria are prioritized based on their intrinsic importance, thereby minimizing subjective preference. When combined with the ARAS method, which ranks alternatives based on their performance ratio to the ideal solution, this approach provides a balanced and comprehensive assessment for each candidate. Based on the results of the evaluation of the barista admission selection, Clara Dewi ranked first with the highest final score of 0.98553, followed by Hanafi Lestari with a score of 0.95921 and Erika Santosa with a score of 0.95726 who ranked second and third.
Co-Authors Aditia Yudhistira Asistyasari, Ayuni Chandra, Iryanto Desyanti Gunawan, Rakhmat Dedi Heni Sulistiani Mesran, Mesran Nuryaman, Yosep Palupiningsih, Pritasari Putra, Ade Dwi Rasli, Roznim Mohamad Ryan Randy Suryono Sanriomi Sintaro Setiawansyah Setiawansyah Sitna Hajar Hadad Subhan Subhan Sufiatul Maryana Sumanto, Sumanto Tri Widodo Ulum, Faruk Very Hendra Saputra Wahyudi, Agung Deni Yuri Rahmanto