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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Reputasi: Jurnal Rekayasa Perangkat Lunak JEECS (Journal of Electrical Engineering and Computer Sciences) TEKNOSIA Bulletin of Informatics and Data Science Jurnal Informatika dan Rekayasa Perangkat Lunak
Junhai Wang
Zhejiang Technical Institute of Economics
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Library & Information Science Transportation

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Decision Support System for Determining Strategic Warehouse Locations Using a Combination of the WENSLO Weighting and RAWEC Method Junhai Wang; Setiawansyah Setiawansyah; Temi Ardiansah; Faruk Ulum; Sumanto Sumanto
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 1, January 2026
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i1.a1456

Abstract

Determining the location of a strategic warehouse is a crucial decision in supply chain management as it directly affects distribution efficiency, logistics costs, and service levels. This problem is multi-criteria and complex, requiring an approach that can accommodate differences in the importance of criteria as well as variations in performance among alternatives objectively. This study aims to develop a Decision Support System to determine a strategic warehouse location by combining the Weights by Envelope and Slope (WENSLO) weighting method and the Ranking of Alternatives with Weights of Criterion (RAWEC) ranking method. The WENSLO method is used to generate criteria weights based on the nonlinear strength of each criterion, while the RAWEC method is applied to calculate the final values and determine the ranking of warehouse location alternatives. A case study was conducted on eleven alternative locations with the main criteria including location cost, accessibility, safety, distribution travel time, and proximity to suppliers. The study results showed that Location TR obtained the highest final score of 0.9673 and was designated as the top priority warehouse location, followed by Location RD with a score of 0.6235 and Location HO with a score of 0.338, while Location QC had the lowest score of −0.975. These findings demonstrate that the combination of the WENSLO and RAWEC methods can produce rankings that are objective, consistent, and easy to interpret, making them a reliable decision-support tool for determining strategic warehouse locations and potentially applicable to other logistics and distribution problems.
Decision Support System for Evaluating Textile Supplier Performance Based on Weights by Envelope and Slope and Mixed Aggregation by Comprehensive Normalization Technique for Multi-Criteria Setiawansyah Setiawansyah; Junhai Wang; Pritasari Palupiningsih; Sufiatul Maryana
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.29131

Abstract

The textile industry is highly dependent on supplier performance in ensuring the quality of raw materials, timely delivery, price stability, and supply continuity. The complexity of supplier evaluation involving many criteria often leads to subjectivity and inconsistencies in decision-making when using conventional approaches. This study proposes a decision support system to evaluate textile supplier performance based on a combination of Weights by Envelope and Slope (WENSLO) and Mixed Aggregation by Comprehensive Normalization Technique for Multi-Criteria (MACONT). The WENSLO method is used to determine the weight of criteria objectively based on data distribution characteristics, while MACONT is applied to assess and rank supplier alternatives through a comprehensive normalization and aggregation process. The case study was conducted involving nine suppliers and five evaluation criteria, namely material quality, timeliness, price, supply capacity, and responsiveness. The results of the study indicate that the proposed model is capable of producing clear and stable supplier rankings, with Supplier A9, Supplier A7, and Supplier A2 occupying the top three positions. These findings demonstrate that the integration of WENSLO and MACONT can enhance the objectivity and consistency of decision-making, as well as provide a more reliable and relevant framework for evaluating textile suppliers to support data-driven supply chain management.
Integration of CRISUS Weighting and ROV Method for Division Head Performance Evaluation in a Manufacturing Company Junhai Wang; Setiawansyah Setiawansyah; Pritasari Palupiningsih
Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Mei 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/reputasi.v7i1.12511

Abstract

The performance evaluation of division heads in manufacturing companies often faces various problems, such as high subjectivity in assessment, the absence of clear criteria weighting standards, and instability in ranking results due to data variation. This situation causes the evaluation results to be less consistent and less able to accurately represent performance. Therefore, this study aims to develop a more objective performance evaluation model by integrating the CRISUS and ROV methods. The CRISUS method is used to determine the criteria weights objectively based on the characteristics of data distribution, while the ROV method is used to rank alternatives by considering variations in performance values through upper and lower bound approaches. The criteria used include leadership, productivity, innovation, operational costs, and error rates. The research results indicate that the proposed model is able to produce more stable and consistent preference values in representing candidate performance. Based on the calculation results, CDT-03 obtained a preference value of 0.4316 and ranked first, followed by CDT-06 with a value of 0.4212 in second place, and CDT-01 in third place with a value of 0.3180. Meanwhile, CDT-02 was in the last position with a value of 0.0739. These findings show that the integration of the CRISUS and ROV methods is able to provide a more objective, comprehensive, and reliable evaluation in supporting managerial decision-making. This research provides several important contributions; this combination is able to overcome the weaknesses of conventional methods by presenting objective criteria weighting as well as a ranking mechanism that takes into account variations in performance conditions.
HYBRID METHOD USING ITARA AND MACONT FOR SELECTING THE BEST CUSTOMERS IN A DECISION SUPPORT SYSTEM Junhai Wang; Setiawansyah Setiawansyah; Riska Aryanti
Teknosia Vol. 20 No. 1 (2026): Vol. 20 No. 01 (2026): June 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research is motivated by the company's challenges in objectively identifying high-value customers due to the numerous assessment criteria, heterogeneous data, and the use of conventional methods that are prone to subjective bias and ranking instability. To address these challenges, this study develops a decision support system based on the hybrid ITARA–MACONT method, where ITARA is used to determine criteria weights rationally based on indifference threshold deviation, while MACONT is applied to perform compromise aggregation in the alternative ranking process. The results show that the system can produce clear and consistent customer rankings, with Customer TY achieving a score of 0.7141 and ranking first, followed by Customer RD with a score of 0.6561 in second place, and Customer AH with a score of 0.5859 in third place. These findings indicate that the integration of ITARA–MACONT is effective in enhancing the objectivity, transparency, and stability of top customer selection results, thereby supporting strategic decision-making aimed at improving customer loyalty and business profitability.
Selection of the Best E-Commerce Platform Based on User Ratings using a Combination Entropy and SAW Methods Faruk Ulum; Junhai Wang; Setiawansyah Setiawansyah; Riska Aryanti
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
Optimizing E-Commerce Platform Selection Using Root Assessment Method and MEREC Weighting Junhai Wang; Dedi Darwis; Rakhmat Dedi Gunawan; Fenty Ariany
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 for Selecting the Best Restaurant Waiter Using a Combination of WENSLO Weighting and AROMAN Methods Riska Aryanti; Junhai Wang; Agung Deni Wahyudi; Setiawansyah Setiawansyah; Dedi Darwis
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 10 No. 2 (2025): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v10i2.4

Abstract

The quality of service staff is a key factor in determining business success because they are the front line that interacts directly with consumers. However, performance evaluations of service staff are often still carried out subjectively, based only on the supervisor's perception or brief experiences with customers. This research discusses the application of a decision support system to determine the best restaurant service by combining the Weights by Envelope and Slope (WENSLO) method in criteria weighting and the Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN) in the alternative ranking process. The dataset used in this study was collected in 2025 from one of the restaurants in the Lampung area, involving nine waiters as evaluation candidates using six criteria. The six criteria used consist of four benefit criteria: service speed, friendliness, accuracy, and customer satisfaction. The weighting results using the WENSLO method indicate that the order mistakes criterion received the highest weight of 0.7253, followed by completion time with a weight of 0.1700, while the other criteria have relatively small weights. The AROMAN method is used to calculate the final values of alternatives based on the specified weights, resulting in a ranking of restaurant servers. The analysis shows that alternative Waiters KS ranks first with the highest score of 1.6097, followed by Waiters QN and Waiters RB. This finding proves that the combination of the WENSLO and AROMAN methods can produce objective, systematic results, and supports restaurant management in making strategic decisions regarding the selection of the best employees.
Co-Authors Dedi Darwis Faruk Ulum Fenty ariany Gunawan, Rakhmat Dedi Palupiningsih, Pritasari Pritasari Palupiningsih Riska Aryanti Setiawansyah Setiawansyah Sufiatul Maryana Sumanto Sumanto Temi Ardiansah Ulum, Faruk Wahyudi, Agung Deni