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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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Decision Support System for Optimizing Supplier Selection Using TOPSIS and Entropy Weighting Methods Yudhistira, Aditia; Wang, Junhai; Rahmanto, Yuri; Setiawansyah, Setiawansyah
Jurnal Pendidikan dan Teknologi Indonesia Vol 4 No 5 (2024): JPTI - Mei 2024
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.456

Abstract

Supplier selection is a crucial process in supply chain management, where companies must determine the best suppliers who are able to meet their needs based on various criteria. Companies often face challenges in managing the various factors that influence supplier selection decisions, suppliers that offer low prices may not always provide the best quality or consistent delivery times. Optimizing supplier selection through the DSS approach, companies can build stronger relationships with high-performing suppliers, while improving overall business resilience and competitiveness. The combination of the TOPSIS method and entropy weighting in supplier selection optimization provides a robust approach to evaluating and selecting the best suppliers based on predetermined criteria. This combination not only improves objectivity and accuracy in the evaluation process, but also allows decision-makers to consider trade-offs between various criteria more effectively. The purpose of the research of the combination of the TOPSIS method and entropy weighting in optimizing supplier selection is to produce objective and data-based criteria weighting through the application of the entropy weighting method, thereby reducing subjectivity in the supplier selection process. The results of the preference value calculated using the TOPSIS method resulted in the first rank with the highest preference value of 0.78393, followed by GH Supplier with a value of 0.75611, and FR Supplier in third place with a value of 0.6913. The next supplier is Supplier AG with a value of 0.59912, followed by Supplier BR with 0.51682, and Supplier TR in sixth position with 0.465. Supplier IH has a preference value of 0.43166, followed by Supplier YS with a value of 0.3984, and finally Supplier RT is in the lowest position with a value of 0.35517. This ranking shows that US Supplier is the best supplier, while Supplier RT is the lowest choice based on the criteria used.
Decision Support System for Platform Selection in E-Commerce Using the OWH-TOPSIS Method Wang, Junhai; Isnain, Auliya Rahman; Suryono, Ryan Randy; Rahmanto, Yuri; Mesran, Mesran; Setiawansyah, Setiawansyah
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.5990

Abstract

Platforms in e-commerce are digital systems that allow online transactions to buy and sell products or services. E-commerce platforms also provide benefits for business actors because they are able to reach a wider market without geographical restrictions, while offering efficiency in business operations. The main problem in choosing a platform for e-commerce is often related to the sheer number of options available and the variety of criteria that must be considered. Criteria such as fees, platform popularity, transaction security, ease of use, features provided, as well as customer service support are important factors in determining the most suitable platform. The implementation of a decision support system to help select the optimal e-commerce platform by applying the OWH-TOPSIS method shows that this system can provide accurate and effective recommendations, so that it can be used as a reference for users in determining the e-commerce platform that suits their needs. The decision support system using the OWH-TOPSIS method provides an efficient and objective solution in the selection of e-commerce platforms. The results of the ranking of the best e-commerce platforms show that Platform D occupies the top position with the highest score value, which is 0.882. In second place is Platform E which obtained a score of 0.8599, followed by Platform A with a score of 0.8341.
Implementation of MABAC Method and Entropy Weighting in Determining the Best E-Commerce Platform for Online Business Wang, Junhai; Darwis, Dedi; Setiawansyah, Setiawansyah; Rahmanto, Yuri
JiTEKH Vol. 12 No. 2 (2024): September 2024
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/jitekh.v12i2.1000

Abstract

The main problem in choosing an e-commerce platform for an online business is finding the one that best suits the specific needs of the business. Each platform has its advantages and disadvantages, such as ease of use, cost, features offered, as well as support for inventory management, shipping, and payments. Other challenges include ensuring that the platform can support future business growth, offer good scalability, and provide flexibility in terms of customization and integration with digital marketing tools. In addition, data security and a good user experience are also important considerations for long-term success. The purpose of this study is to implement the MABAC method and Entropy weighting in determining the best e-commerce platform for online businesses, so that this research can provide clear and data-driven recommendations to stakeholders regarding the most effective e-commerce platform. The application of the MABAC method combined with Entropy weighting in determining the best e-commerce platform for online business people offers a comprehensive and objective approach in decision-making. This combination not only improves decision-making accuracy, but also ensures that the most important criteria are weighted accordingly, resulting in more reliable results in choosing the best platform for business needs. The final result of the MABAC Platform A score is the first choice, considering the highest score of 0.82, which indicates its optimal performance in meeting the criteria that have been set. In addition, Platforms B and C, with scores of 0.78 and 0.75, respectively.
Decision Support System for Choosing the Best Shipping Service for E-Commerce Using the SAW and CRITIC Methods Wang, Junhai; Setiawansyah, Setiawansyah; Rahmanto, Yuri; Asistyasari, Ayuni
Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) Vol. 3 No. 2 (2024): Volume 3 Number 2 September 2024
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jima-ilkom.v3i2.32

Abstract

Choosing the best delivery service for e-commerce is a crucial step that can affect customer satisfaction and overall business success. In today's digital era, consumers expect fast, secure, and affordable delivery. The main problem in choosing the best shipping service for e-commerce is often related to several interrelated factors, which can affect the customer experience and business sustainability. One of the biggest challenges is ensuring that products reach customers within the promised time. Delays in delivery can lead to customer dissatisfaction and potentially damage a business's reputation. The combination of SAW and CRITIC methods provides a powerful approach to multi-criteria decision-making. By leveraging the advantages of each method, users can objectively determine the weight of the criteria and evaluate alternatives in a systematic and transparent way. This approach not only improves the accuracy of decisions but also increases decision-makers confidence in the results obtained. Based on the results of the ranking using the method that has been applied, the alternative with the highest score is Pos Indonesia (A6) with a final score of 0.82506, followed by JNE (A1) with a score of 0.76181, and Tiki (A2) with a score of 0.72127. Based on these values, Pos Indonesia ranks first as the best service provider.
DYNAMIC WEIGHT ALLOCATION IN MODIFIED MULTI-ATRIBUTIVE IDEAL-REAL COMPARATIVE ANALYSIS WITH SYMMETRY POINT FOR REAL-TIME DECISION SUPPORT Hadad, Sitna Hajar; Chandra, Iryanto; Wang, Junhai; Megawaty, Dyah Ayu; Setiawansyah, Setiawansyah; Yudhistira, Aditia
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4170

Abstract

Decision Support Systems (DSS) have a crucial role in real-time decision-making, especially in the digital era that demands high speed and accuracy. Managing criterion weights in a dynamic environment presents significant challenges due to rapid and unpredictable changes in conditions. However, determining an accurate weight becomes difficult due to uncertainty, incomplete data, and subjective factors from decision-makers. In addition, changes in the external environment, such as market trends, regulations, or customer preferences, can affect the relevance of each criterion, thus requiring a real-time weight adjustment mechanism. The purpose of this study is to develop and explore the dynamic weight allocation method in symmetry point- multi-attributive ideal-real comparative analysis (S-MAIRCA) to support more accurate and responsive real-time decision-making in a dynamic environment. This research contributes to the understanding of how the weights of criteria can be adjusted automatically and responsively to changing conditions or new data, which increases the relevance and accuracy of decisions in a dynamic environment. The urgency of S-MAIRCA research is important because it often involves real-time, dynamic, and complex data. This development not only improves the adaptability of the S-MAIRCA method, but also contributes significantly to creating computer science-based applications that are more intelligent, flexible, and relevant to the evolving needs of the system. The results of the alternative ranking comparison using the CRITIC-MAIRCA, LOPCOW-MAIRCA, ROC-MAIRCA, and S-MAIRCA methods showed variations in the ranking order generated for each alternative using spearman correlation. The results of the correlation value of CRITIC-MAIRCA and LOPCOW-MAIRCA have a very high correlation of 0.993, which shows that these two methods provide almost identical rankings in alternative evaluation. Likewise, CRITIC-MAIRCA and S-MAIRCA had a high correlation of 0.979, signaling a strong similarity in ranking results despite slight differences in the approaches used by the two methods. The results of the application of the MAIRCA-S method in the development of DSS based on real-time data have a significant impact on improving the speed, accuracy, and adaptability of decisions. MAIRCA-S strengthens the validity of decision results by considering a variety of attributes on a more comprehensive scale, providing added value in the development of DSS for various industrial sectors.
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.
Multi-Criteria Decision Support System for Best Warehouse Performance Selection Using Combined Compromise Solution Method Wang, Junhai; Setiawansyah, Setiawansyah; Isnain, Auliya Rahman
Bulletin of Data Science Vol 4 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletinds.v4i2.7196

Abstract

Selecting the best performing warehouse is a strategic step in supporting the efficiency of the supply chain and distribution of goods. This research aims to design a multi-criterion-based decision support system in evaluating and determining the best warehouse using the Combined Compromise Solution (CoCoSo) method. This method was chosen for its ability to combine the strength of weighted average approaches and relative compromises between alternatives, resulting in more balanced and objective decisions. This research involves eight warehouse alternatives that are assessed based on a number of relevant performance criteria. The process starts from problem identification, determination of criteria, data collection, normalization, weighting, to the application of the CoCoSo method. The final results showed that Warehouse C obtained the highest score of 4.8155, followed by Warehouse E and Warehouse A, indicating that the three warehouses had the best performance. These findings are expected to be a reference in strategic decision-making related to warehousing management as well as the basis for the development of a data-based performance evaluation system.
Selection of the Best E-Commerce Platform Based on User Ratings using a Combination Entropy and SAW Methods Ulum, Faruk; Wang, Junhai; Setiawansyah, Setiawansyah; Aryanti, Riska
Bulletin of Informatics and Data Science Vol 3, No 2 (2024): November 2024
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v3i2.92

Abstract

Choosing the right e-commerce platform has a crucial role for consumers and business actors. For consumers, a reliable and user-friendly platform provides a safe, convenient, and efficient shopping experience. Considering various aspects of choosing the right e-commerce platform is a strategic investment that can provide long-term added value for all parties involved in the digital ecosystem. The purpose of this study is to identify and determine the best e-commerce platforms based on user experience and assessment with an objective and structured decision-making approach using a combination of Entropy and SAW methods. The results of the ranking of the best e-commerce platform selection determined through the combination of the Entropy and SAW methods, obtained that Shopee ranked first with the highest preference value of 0.9819, followed by Tokopedia in second place with a value of 0.973. Furthermore, Blibli is in third place with a score of 0.9401, followed by Lazada with a score of 0.9305, and the last is Bukalapak with a score of 0.9021. This research makes a significant contribution to multi-criteria decision-making by applying a combination of Entropy and SAW methods to evaluate and determine the best e-commerce platform based on user assessments. The results of this research can be used as a practical reference as a basis for strategic decision-making in choosing the e-commerce platform that best suits market needs
Hybrid G2M Weighting and WASPAS Method for Business Partner Selection: A Decision Support Approach Wang, Junhai; Setiawansyah, Setiawansyah; Alita, Debby
Journal of Computer System and Informatics (JoSYC) Vol 6 No 3 (2025): May 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i3.7229

Abstract

Choosing the right business partner is a crucial factor in the success and continuity of a company's operations. The main issue in selecting business partners is the complexity of balancing various interconnected and often conflicting factors. Another problem lies in the subjectivity and limitations of information. Evaluators or decision-makers may have differing views on the priority of criteria or the interpretation of the available data. This study proposes a hybrid method-based decision support system approach that combines G2M Weighting and WASPAS to address the challenges in complex and uncertain multi-criteria evaluations. The G2M method is used to objectively determine the weight of criteria based on geometric averages in gray environments, so as to be able to capture data variability and uncertainty. Furthermore, the WASPAS method is applied to calculate the final value and rank the alternative business partners based on a combination of additive and multiplicative approaches. The ranking chart for business partner selection using the G2M Weighting and WASPAS method shows that Partner G gets the highest score of 9.93E+03, followed by Partner A and Partner E who have the same score of 9.43E+03. Meanwhile, Partner D had the lowest score, which was 5.97E+03. This ranking of business partner selection shows that Partner G is the best choice as a business partner based on the evaluation method used. The results of the study show that this hybrid approach provides more accurate, stable, and comprehensive evaluation results than conventional methods. This approach can be an effective solution for companies in supporting the strategic decision-making process in choosing the best business partners.
An Entropy-Assisted COBRA Framework to Support Complex Bounded Rationality in Employee Recruitment Oprasto, Raditya Rimbawan; Wang, Junhai; Pasaribu, A Ferico Octaviansyah; Setiawansyah, Setiawansyah; Aryanti, Riska; Sumanto
Bulletin of Computer Science Research Vol. 5 No. 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i3.505

Abstract

In the employee recruitment process, decision-making often involves many criteria and relies on the subjective judgment of the decision-maker. The main problem lies in how to develop a decision support system that can overcome this complexity while maintaining rationality and objectivity. This study aims to apply a hybrid framework based on the entropy and COBRA methods to support objective decision-making in the employee recruitment process, and to overcome the limitations of subjectivity and bounded rationality in candidate selection with a structured data-driven approach. The entropy method is used to objectively determine the weight of criteria based on data variations, thereby helping to reduce subjectivity in decision-making and increase the rationality of COBRA analysis results. The results of the final calculation using the Entropy-COBRA method, were ranked nine candidates based on their final scores which reflected relative proximity to the ideal solution in the recruitment process. The candidate with the lowest score is considered to be the closest to the ideal solution and has the best overall performance. Raka employees ranked first with a final score of -0.0618, followed by Andra in second place with a score of -0.0597, and Fajar in third place with -0.0357. The results of the final score in the COBRA method with a lower score indicate that an alternative shows superior performance over the other. This framework makes a real contribution to data-driven decision-making for human resource management, particularly in the context of recruitment involving multiple criteria and alternatives.
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 Sumanto, Sumanto Ulum, Faruk Wahyudi, Agung Deni Yuri Rahmanto