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Journal of Decision Support Systems and Multi-Criteria Decision Making (JODESMA)
Published by Asosiasi Peneliti Informatika dan Komputer untuk Riset
ISSN : -     EISSN : 31647022     DOI : https://doi.org/10.67449/jodesma
Core Subject :
The Journal of Decision Support Systems and Multi-Criteria Decision Making is a scientific journal dedicated to the development, application, and advancement of decision support systems and multi-criteria decision-making approaches. It provides a scholarly platform for researchers, academics, practitioners, and professionals to disseminate original research addressing complex decision-making problems through systematic, quantitative, computational, and analytical approaches. The journal welcomes research that develops and applies decision models, mathematical methods, computational techniques, data-driven approaches, optimization methods, and decision analysis frameworks to support effective, transparent, and reliable decision-making. Particular emphasis is placed on problems involving multiple alternatives, multiple criteria, conflicting objectives, uncertainty, preferences, and complex decision environments. The journal publishes original research covering theoretical developments, methodological innovations, computational models, system development, empirical studies, comparative analyses, sensitivity analysis, and real-world applications of decision support and multi-criteria decision-making methods across diverse domains.
Arjuna Subject : -
Articles 10 Documents
 1 
Decision Support System for Production Machine Maintenance Prioritization Using LOPCOW Weighting and SPOTIS Very Hendra Saputra; Agung Deni Wahyudi
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 1 (2026): November 2026
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i1.1

Abstract

Maintenance of production machines is essential for maintaining production continuity and minimizing the risk of downtime and operational losses. However, limited maintenance resources, including budget, technician availability, maintenance time, and spare parts, require organizations to establish appropriate maintenance priorities. This study proposes a Decision Support System (DSS) for objectively prioritizing production machine maintenance using a Multi-Criteria Decision-Making (MCDM) approach that integrates the LOPCOW and SPOTIS methods. LOPCOW is applied to determine objective criterion weights based on the characteristics and variation of the maintenance data, while SPOTIS is used to rank production machines according to their distance from the ideal solution. The results show that C7 obtains the highest criterion weight of 0.2616, followed by C5 (0.2134) and C6 (0.1937), while C2 obtains the lowest weight of 0.0412. The SPOTIS results produce the maintenance priority sequence M4, M7, M2, M5, M1, M8, M3, and M6, with M4 achieving the highest priority with a final value of 0.0000, while M6 has the lowest priority with 1.0000. These findings demonstrate that the proposed LOPCOW–SPOTIS framework can provide a systematic, objective, and transparent basis for identifying maintenance priorities and supporting more consistent allocation of limited maintenance resources.
Comparative Analysis of Objective Weighting Approaches in Multi-Criteria Decision Making Using WASPAS Sanriomi Sintaro; Pritasari Palupiningsih; Junhai Wang
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 1 (2026): November 2026
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i1.2

Abstract

Selecting the best-performing honorary employee is a multi-criteria decision-making problem because employee performance is influenced by several criteria with different levels of importance. This study aims to conduct a comparative analysis of objective weighting approaches in the Weighted Aggregated Sum Product Assessment (WASPAS) method for evaluating honorary employee performance and examining the stability of the resulting rankings. Eight alternatives, namely HE-A to HE-H, were evaluated using five criteria: Work Performance, Discipline, Cooperative Ability, Initiatives and Problem Solving, and Communication Skills. Twelve objective weighting methods, namely Entropy, G2M, RECA, LOPCOW, CRITIC, MEREC, WENSLO, LOGSTA, LODECI, ITARA, SITDE, and DAM, were independently applied to determine criterion weights. The resulting weights were then integrated into WASPAS to generate alternative rankings under different weighting scenarios. The results show that HE-F consistently achieved the first rank across all objective weighting approaches, while HE-E consistently occupied the eighth position. The ranking comparison further indicates that most methods produce ranking patterns close to the original ranking, although MEREC introduces greater changes in several alternative positions. Spearman rank correlation analysis confirms a strong consistency of the resulting rankings, with the highest correlation obtained by LODECI, SITDE, and DAM at 0.9524, followed by G2M and CRITIC at 0.9286 and MEREC at 0.9048. Overall, the findings demonstrate that the combination of objective weighting approaches and WASPAS provides a reliable framework for employee performance evaluation, while comparative correlation analysis can be used to assess ranking stability and robustness across different weighting perspectives.
Combination of Criteria Importance Through Intercriteria Dependence and Simple Additive Weighting Methods for Multi-Criteria Barista Selection: A Decision Support Approach Verra Sofica; Titik Misriati
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 1 (2026): November 2026
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i1.3

Abstract

Barista selection is an important process in the coffee shop and hospitality industry because baristas are required to possess not only technical skills but also coffee knowledge, communication, work speed, accuracy, and creativity. Evaluating candidates based on a single criterion can result in subjective and less representative decisions. Therefore, this study aims to develop a Decision Support System (DSS) for barista candidate selection by integrating the Criteria Importance Through Intercriteria Dependence (CRITID) and Simple Additive Weighting (SAW) methods. CRITID is applied to determine objective criterion weights by considering data variation and inter-criteria relationships, while SAW is used to calculate preference values and rank the candidates. The results show that Work Speed (CB-06) has the highest criterion weight of 0.1311, followed by Technical Skills (CB-02) with 0.1275 and Coffee Knowledge (CB-04) with 0.1253. The SAW ranking identifies A7 as the highest-ranked candidate with a preference value of 0.9682, followed by A3 with 0.9603 and A8 with 0.8994. Sensitivity analysis involving 32 scenarios with criterion weight changes of ±0.05 and ±0.10 indicates that the ranking structure is relatively stable, with A7 and A3 consistently maintaining the first and second positions. These findings demonstrate that the integration of CRITID and SAW can support a more objective, systematic, measurable, and stable decision-making process for barista candidate selection.
A Novel MPSI Weighting and Proximity Indexed Value Framework for Multi-Criteria Selection of Marketplace Logistics Partners Sufiatul Maryana; Arie Qurania; Agung Prajuhana Putra
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 1 (2026): November 2026
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i1.4

Abstract

In facing the increasingly intense competition in the logistics industry, companies are often confronted with challenges in selecting the most appropriate logistics partner due to the many performance criteria that must be considered simultaneously. This study aims to evaluate and determine the best logistics partner using a Multi-Criteria Decision-Making approach through the integration of the MPSI method for criteria weighting and PIV for the alternative ranking process. The criteria used include Transit Time, On-Time Delivery, First-Attempt Success, Attempt Rate, and Package Volume, which are analyzed based on the performance data of each alternative. The weighting results indicate that Package Volume and Transit Time are the most dominant factors in the evaluation process. Based on the PIV method calculations, the ranking results were obtained, with NJ Logistics Partner occupying the first rank with a score of 0.46408, followed by TK Logistics Partner in second place with a score of 0.44014, and JT Logistics Partner in third place with a score of 0.39282. Sensitivity tests through various weight change scenarios showed that the ranking structure remained consistent, indicating that the model has a high level of stability and resilience to parameter variations. Overall, the proposed approach is able to produce decisions that are objective, measurable, and reliable as a basis for supporting strategic logistics partner selection.
Hybrid Modification Preference Selection Index with Optimized Pairwise Ratio Analysis Method for Multi-Criteria Decision Making: An Integrated Weighting and Ranking Approach Tri Widodo; Dedi Darwis; Shaban Nassor
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 1 (2026): November 2026
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i1.5

Abstract

Multi-criteria job candidate selection requires an objective and systematic approach because candidates are evaluated using multiple criteria with different levels of importance. This study proposes a hybrid Modification Preference Selection Index with Optimized Pairwise Ratio Analysis (MPSI–OPARA) framework that integrates objective criteria weighting and alternative ranking. MPSI is used to determine criterion weights based on normalized performance variation, while OPARA is applied to rank candidates through adjusted pairwise comparisons. The framework is demonstrated using six job candidates evaluated across six criteria: educational background, work experience, technical competence, communication skills, problem-solving ability, and interview performance. The results show that educational background and work experience are the dominant criteria, with weights of 0.5600 and 0.3422, respectively. OPARA produces Candidate IMS as the first-ranked candidate, Candidate GTB as the second-ranked candidate, and Candidate RDH as the third-ranked candidate. Furthermore, comparisons with SAW, SMART, WASPAS, and MOORA produce identical ranking positions, demonstrating consistent ranking outcomes. These findings indicate that the proposed MPSI–OPARA framework provides a systematic and consistent approach for objective criteria weighting and job candidate selection.
Enhanced Root Assessment Method for Multi-Criteria Decision Making: An Improved Framework for Robust Alternative Ranking Aditya Lapu Kalua; Angreine Kewo; Susi Hendartie; Yampi R Kaesmetan
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 2 (2027): March 2027
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i2.6

Abstract

Multi-Criteria Decision Making (MCDM) methods are widely used to address complex decision problems involving multiple alternatives and criteria with different characteristics. However, ranking outcomes may be sensitive to normalization mechanisms, criterion weights, and aggregation processes, particularly when benefit and cost criteria are evaluated simultaneously. This study proposes the Enhanced Root Assessment Method (ERAM), an improved root-based MCDM framework designed to provide a more structured and balanced evaluation of alternative performance while improving ranking stability. ERAM integrates modified normalization, weighted normalization, positive and negative aggregation, Relative Benefit–Penalty Balance, and an Enhanced Root Assessment Index to construct the final preference measure. The proposed method is evaluated using a new store location selection dataset comprising eight alternatives and six criteria. Its performance is examined through comparative analysis with SAW, WASPAS, GRA, and MOORA, together with sensitivity analysis using ±0.05 changes in criterion weights. The results show that ERAM maintains a stable ranking structure under weight variations and achieves a strong Spearman correlation of 0.9286 with the reference ranking, exceeding WASPAS (0.9048) and SAW, GRA, and MOORA (0.8810). These findings demonstrate that ERAM can produce consistent rankings while maintaining robustness against moderate changes in criterion importance. Therefore, ERAM provides a structured and robust framework for multi-criteria ranking problems involving heterogeneous criteria and uncertain decision preferences.
TEKNO Method: Total Evaluation based on Knowledge-driven Normalized Optimization for Multi-Criteria Decision Making Yuri Rahmanto; Ryan Randy Suryono; Dedi Darwis; Abhishek R. Mehta; Auliya Rahman Isnain
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 2 (2027): March 2027
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i2.7

Abstract

Multi-criteria decision making (MCDM) methods play an important role in supporting decisions involving multiple criteria with different characteristics and levels of importance. However, differences in normalization procedures, criterion treatment, and aggregation mechanisms can lead to variations in the resulting preference structures. This study proposes a new MCDM method, namely total evaluation based on knowledge-driven normalized optimization (TEKNO), which integrates normalization, relative evaluation, criterion weighting, optimization-based normalization, and total evaluation into a unified decision-making framework. The proposed method is designed to transform heterogeneous decision information into comparable evaluation values while preserving the relative contribution of each criterion. The applicability of TEKNO is evaluated through two decision-making case studies involving new store location selection and leasing customer selection. The evaluation framework includes ranking analysis, comparison with established MCDM methods, Spearman rank correlation analysis, and sensitivity analysis under variations in criterion weights. The results show that TEKNO achieves a Spearman rank correlation coefficient of 1.0000 for the new store location case and 0.9964 for the leasing customer selection case, indicating very strong agreement with the reference rankings. In addition, the ranking remains unchanged across the tested sensitivity scenarios, demonstrating the stability of TEKNO under variations in criterion weights. These findings indicate that TEKNO provides a transparent, systematic, and stable alternative for MCDM applications for practical decision support where reliable ranking, methodological transparency, and robustness across alternative evaluation conditions are required. Nevertheless, broader validation using diverse datasets, decision domains, weighting schemes, and statistical evaluation techniques is required to further establish its generalizability and comparative performance.
Integrating LODECI Weighting and ALPAS Methods for Raw Material Supplier Selection in the Textile Industry Pritasari Palupiningsih; Meilia Nur Indah Susanti; Herman Bedi Agtriadi
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 2 (2027): March 2027
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i2.8

Abstract

The selection of raw material suppliers in the textile industry is a complex challenge because it involves various criteria, such as price, delivery timeliness, material quality, color consistency, and production capacity. Subjective decisions or those based on unstructured data often lead to inaccuracies, unfairness, and supply chain disruption risks. This study aims to present an objective and systematic approach by integrating the LODECI method for criteria weighting and ALPAS for alternative ranking. The LODECI method extracts criteria weights rationally from variations in supplier performance, minimizing the influence of subjectivity, while ALPAS combines the criteria weights with the performance data of alternatives to generate final scores and rank suppliers comprehensively. This study involved eleven actual suppliers as alternatives, with performance data reflecting real operational conditions. The ranking results show that alternative A8 has superior and stable performance, followed by A10 and A4, while the sensitivity analysis indicates that changes in criteria weights of ±0.05 do not cause significant shifts in rankings, confirming the model's robustness. These findings demonstrate that the integration of LODECI and ALPAS can enhance the objectivity, consistency, and accountability of supplier decision-making, supporting supply chain efficiency, product quality, and the overall competitiveness of the textile industry.
Benchmarking Aggregation-Based MCDM Methods: A Comparative Analysis of Ranking Consistency, Stability, and Robustness Sumanto Sumanto; Mochamad Wahyudi; Lise Pujiastuti
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 2 (2027): March 2027
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i2.9

Abstract

Multi-Criteria Decision Making (MCDM) methods are widely applied in Decision Support Systems (DSS) to address selection problems involving multiple criteria. However, differences in aggregation mechanisms may affect ranking consistency, stability, and robustness under changing decision conditions. This study presents a benchmarking analysis of five aggregation-based MCDM methods, namely Simple Additive Weighting (SAW), Weighted Aggregated Sum Product Assessment (WASPAS), Additive Ratio Assessment (ARAS), Complex Proportional Assessment (COPRAS), and Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA), for employee selection. Ranking consistency is evaluated using Spearman rank correlation, while stability and robustness are assessed using the Ranking Stability Index and Ranking Retention Rate. SAW, WASPAS, ARAS, and COPRAS achieve perfect consistency with the reference ranking, with a Spearman coefficient of 1.0000, while MAIRCA obtains 0.9879. ARAS and COPRAS achieve the highest stability, with an RSI of 97.22%, and robustness, with an RRR of 86.11%. SAW, WASPAS, and MAIRCA achieve an RSI of 95.56% and an RRR of 77.78%. Ranking changes are mainly limited to an exchange between Candidates 1 and 9, while Candidate 3 remains first across all scenarios. These findings suggest that ARAS and COPRAS exhibit comparatively higher ranking stability under the experimental conditions considered in this study.
Adaptive Balanced Entropy Weighting for Mitigating Criterion Weight Imbalance in Multi-Criteria Decision Making Hetty Rohayani; Kevin Kurniawansyah; Rahmi Handayani; Mohamad Nizam Yusof
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 2 (2027): March 2027
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i2.10

Abstract

Multi-Criteria Decision Making (MCDM) relies on criterion weights to represent the relative importance of evaluation criteria in ranking alternatives. However, conventional Entropy Weighting may generate highly imbalanced weights when criteria exhibit substantially different levels of information variation, potentially causing excessive dominance of particular criteria. This study proposes Adaptive Balanced Entropy (A-Entropy), an objective weighting method that integrates the information-based principle of Entropy Weighting with an adaptive balancing mechanism. The method derives entropy-based weights, measures weight imbalance using a Weight Imbalance Index, and adaptively adjusts the weights according to the identified imbalance level. The effectiveness of A-Entropy is evaluated through two MCDM case studies involving lecturer selection and leasing customer selection. The resulting weights are compared with conventional Entropy Weighting, while their effects on alternative rankings are evaluated using MOORA and PIV, respectively. The results show that A-Entropy substantially reduces weight concentration in the lecturer selection case, where the highest weight decreases from 0.4368 to 0.3333 and the lowest weight increases from 0.0042 to 0.0665. In the leasing customer selection case, the adjustment is more moderate due to its comparatively lower initial imbalance. Furthermore, A-Entropy produces higher Spearman correlations with the original rankings than Entropy, increasing from 0.8303 to 0.9030 for lecturer selection and from 0.9758 to 0.9879 for leasing customer selection. These findings indicate that A-Entropy reduces excessive criterion weight imbalance while preserving meaningful differences in criterion importance and maintaining high ranking consistency. Therefore, A-Entropy provides an adaptive objective weighting approach for MCDM-based Decision Support Systems.

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