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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 15 Documents
 1   2 
Comparative Evaluation of Ratio-Based MCDM Methods: Performance and Ranking Stability under DAM Weighting Mahardika Inra Takaendengan; Irma Anggraeni; Febri Dwi Irawati; Gde Brahupadhya Subiksa
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 3 (2027): July 2027
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i3.11

Abstract

Ratio-based Multi-Criteria Decision-Making (MCDM) methods provide practical approaches for ranking alternatives, but differences in normalization and aggregation mechanisms can produce different rankings and levels of stability. This study comparatively evaluates ratio-based MCDM methods under a common Data Assessment Method (DAM) weighting framework, focusing on MOORA and ARAS for logistics hub location selection. The study uses a dataset comprising 12 candidate logistics hub locations and six criteria. DAM is first applied to derive criterion weights, after which MOORA and ARAS are implemented using the same decision matrix and weighting configuration. Ranking performance is examined through baseline ranking comparison, while robustness is assessed using systematic criterion-weight perturbations of ±0.05 and ±0.10. Kendall’s Tau is employed to measure ranking consistency, and Mean Absolute Rank Change (MARC) is used to evaluate ranking stability. The DAM results show a relatively balanced criterion-weight distribution, with CLH02 receiving the highest weight of 0.1932. Both methods identify LH6 as the best alternative and LH9 as the least preferred, although several intermediate alternatives exchange positions. MOORA achieves a Kendall’s Tau of 0.9163 and a MARC of 0.3715, whereas ARAS obtains 0.9674 and 0.1840, respectively. These results demonstrate that ARAS provides higher ranking consistency and substantially greater stability under weight perturbations. Therefore, ARAS is identified as the better-performing ratio-based MCDM method for the evaluated logistics hub location problem. The findings highlight the importance of assessing both ranking consistency and stability when selecting MCDM methods for robust decision support.
Development of the Range of Value (ROV) Method Based on Dynamic Weighting for Decision Support Systems Erlin Windia Ambarsari; Muhammad Waqas Arshad; Gustian Rama Putra; Yuwan Jumaryadi; Febryo Ponco Sulistyo
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 3 (2027): July 2027
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i3.12

Abstract

Decision-making in complex environments requires an evaluation framework capable of integrating multiple criteria with different levels of importance and performance. This study proposes Dynamic-ROV, a modified Rank Order Value approach designed to evaluate alternatives through data-driven criterion weighting and utility-based assessment. The proposed procedure consists of decision matrix normalization, standard deviation-based criterion weighting, best and worst utility evaluation, final utility calculation, and ranking. The approach was applied to the selection of nine manager candidates using seven evaluation criteria. Its robustness was assessed through 28 sensitivity scenarios involving positive and negative changes in individual criterion weights, while its ranking consistency was further examined against SAW, MOORA, GRA, and SMART. The results show that Candidate M-02 achieves the highest final utility value of 0.3862 and obtains the first rank, whereas Candidate M-08 records the lowest utility value of 0.1179 and ranks ninth. The sensitivity analysis indicates that the principal ranking structure remains stable across all tested scenarios, with M-02 consistently occupying the first position and M-08 remaining in the last position. Comparative evaluation also demonstrates that Dynamic-ROV produces ranking patterns that are broadly consistent with the established MCDM methods. These findings indicate that Dynamic-ROV provides a reliable and stable framework for multi-criteria evaluation while offering a data-oriented mechanism for determining criterion importance and assessing alternative performance. The proposed approach can therefore serve as an alternative decision-support method for selection problems in organizational settings.
HIVE Weighting: A Novel Heterogeneity-Aware Approach for Criteria Weighting in Multi-Criteria Decision Making Nirwana Hendrastuty; Aditia Yudhistira; Ari Sulistiyawati; Ahmad Ari Aldino
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 3 (2027): July 2027
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i3.14

Abstract

Objective criterion weighting plays an important role in multi-criteria decision-making (MCDM), particularly when decision outcomes depend on how differences among criteria are represented. However, conventional objective weighting approaches may emphasize specific statistical characteristics of the decision matrix without explicitly considering criterion heterogeneity as an integrated concept. This study proposes Heterogeneity-Informed Value Emphasis (HIVE) Weighting, a Heterogeneity-Informed Value Emphasis approach, for objective criterion weighting in MCDM. The method incorporates the mean, standard deviation, and relative variability of normalized criterion values to construct a heterogeneity-based measure of criterion importance. The resulting weights are then integrated with established MCDM methods to examine their applicability in alternative evaluation. A comparative analysis is conducted against Entropy, WENSLO, LOGSTA, CRITIC, and MEREC, followed by evaluations using the SAW and MOORA methods. Sensitivity analysis is further performed by varying criterion weights across multiple perturbation scenarios to examine the stability of the resulting rankings. The findings indicate that HIVE generates a more balanced representation of criterion importance than several comparative weighting approaches while preserving compatibility with different MCDM aggregation procedures. The ranking results obtained through SAW and MOORA show consistent patterns, while the sensitivity analysis indicates that the overall ranking structure is relatively stable under changes in criterion weights. These results demonstrate that HIVE provides an interpretable and flexible framework for incorporating criterion heterogeneity into objective weighting. The proposed approach offers a basis for further research on heterogeneity-aware weighting across diverse MCDM datasets and decision-making contexts, supporting more reliable and transparent weighting practices in complex decision environments.
Logarithmic Normalization and Standard Deviation Modification for Enhanced Sensitivity in Objective Criteria Weighting Vederico Pitsalitz Sabandar; Yulian Hany Makaruku; M. Ikbal Siami; Sandi Badiwibowo Atim; Sanriomi Sintaro
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 3 (2027): July 2027
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i3.15

Abstract

Objective weighting methods are essential in multi-criteria decision-making (MCDM) because criterion weights directly influence alternative rankings. However, dispersion-based weighting approaches may assign excessive importance to criteria with high variability, resulting in an imbalanced weighting structure. This study proposes LOGSTA-D, an extension of the Logarithmic Normalization and Standard Deviation (LOGSTA) method that introduces a Dispersion Compression Transformation based on the square root of standard deviation. The transformation is designed to moderate differences in criterion dispersion before logarithmic dispersion is used to derive the final criterion weights. The proposed method is evaluated using a supplier selection dataset consisting of nine alternatives and eight criteria and is integrated with four MCDM methods, namely SAW, MOORA, WP, and WASPAS. The results show that LOGSTA-D produces a more moderated distribution of criterion weights by reducing the dominance of highly dispersed criteria and redistributing importance toward criteria with lower initial weights. Across the evaluated MCDM methods, the resulting rankings exhibit a high degree of consistency, with limited changes among intermediate alternatives. The robustness analysis shows that the LOGSTA-D with MCDM framework achieves a robustness value of 0.9875, compared with 0.9604 for the LOGSTA with MCDM framework, indicating a higher level of ranking consistency under the evaluated conditions. These findings demonstrate that LOGSTA-D provides a controlled dispersion-based weighting mechanism that can support robust decision-making in supplier selection problems.
Modification of the Proximity Indexed Value Method Based on Distance-Based Normalization to Improve the Stability of Alternative Rankings Desyanti Desyanti; Gellysa Urva; Sunggito Oyama; Kristina Febriani
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 3 (2027): July 2027
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i3.16

Abstract

The Proximity Indexed Value (PIV) method provides a proximity-based mechanism for ranking alternatives in multi-criteria decision-making; however, its ranking results can be affected by the normalization process and the relative characteristics of criterion values. This study proposes a Distance-Based Proximity Indexed Value (D-PIV) method by incorporating distance-based normalization into the PIV framework to represent the proximity of alternatives to criterion-specific ideal conditions more explicitly. The proposed method was evaluated using an alternative location selection dataset comprising eight alternatives and six criteria, including one cost criterion and five benefit criteria. The evaluation included comparative ranking analysis using 36 systematic criterion-weight perturbation scenarios with changes of ±0.05, ±0.10, and ±0.15. The results show that D-PIV changes the ordering of Loc 3 and Loc 8 compared with conventional PIV, resulting in a ranking that fully corresponds to the reference ranking. The Spearman correlation increased from 0.9762 for PIV to 1.0000 for D-PIV. However, the sensitivity analysis indicates that the distance-based modification does not uniformly improve ranking stability across all alternatives. D-PIV increases the stability of Loc 8 from 69.44% to 72.22%, while lower stability is observed for Loc 3 and Loc 6 compared with PIV. These findings demonstrate that D-PIV contributes a reference-oriented normalization mechanism that improves baseline ranking consistency while redistributing, rather than uniformly eliminating, sensitivity to criterion-weight variations. The study therefore provides both a methodological contribution through the integration of distance-based normalization into PIV and an empirical contribution through systematic evidence of its effects on ranking agreement and stability.

Page 2 of 2 | Total Record : 15

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