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Journal of Analytical Uncertainty
Published by Yayasan Winaya Inspirasi Nusantara
ISSN : 31246818     EISSN : 31246818     DOI : https://doi.org/10.63924/jau
Core Subject : Science, Education, Engineering,
Computer Science & IT Engineering Mathematics
The Journal of Analytical Uncertainty (JAU) is an international, peer-reviewed, multidisciplinary journal devoted to advancing theoretical, computational, and applied research applied research on randomness and uncertainty in decision-making. The journal provides a platform for researchers, academicians, and practitioners working in the diverse domains of mathematics, statistics, and fuzzy sciences to exchange innovative ideas and findings addressing the complexities of uncertain, imprecise, and vague information. The JAU aims to promote the integration of analytical modeling, statistical inference, and uncertainty quantification for enhanced decision-making across various fields including economics, finance, engineering, healthcare, environmental studies, and social sciences. The journal welcomes contributions that develop new methods, propose models, or present applications in which uncertainty plays a critical role in decision processes.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 12 Documents
 1   2 
Shortest Path Problem in Network with Quadripartitioned Neutrosophic Arc Length Broumi Said; Yuhao Su; M. Parimala; Yekini Shehu
Journal of Analytical Uncertainty Vol. 1 No. 1 (2025): JAU: December 2025
Publisher : Winaya Inspirasi Nusantara Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63924/jau.v1i1.262

Abstract

Many extensions and generalizations of neutrosophic sets have been introduced and studied in the literature. Quadripartitioned single valued neutrosophic set becomes an important tool in solving various types of decision making problems, medical diagnosis problems, clustering,... etc. The neutrosophic graph has still been a powerful tool for modeling and designing indeterminate networks. Quadripartitioned single valued neutrosophic graph is a generalization of single valued neutrosophic graph. In this chaptre, we formulate the shortest path (SP) problem in Quadripartitioned single valued neutrosophic environment. Here, the costs related to arcs are taken in the form of Quadripartitioned single valued neutrosophic numbers (QSVNNs). A numerical example also included illustrating our proposed method for finding the neutrosophic shortest path.
Super Rhotrix Driven Multi-criteria Decision Making R. Priya; Nivetha Martin; Florentine Smarandache
Journal of Analytical Uncertainty Vol. 1 No. 1 (2025): JAU: December 2025
Publisher : Winaya Inspirasi Nusantara Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63924/jau.v1i1.263

Abstract

This study introduces the theoretical conceptualization of Super Rhotrix and its applications in multi-criteria decision making. The properties, operations and classifications of Super Rhotrix are discoursed in this work. The geometrical interpretation of the structures of rhotrix and super rhotrix are described in the context of applications to decision-making. The rigidity and compatibility of super rhotrix is well -substantiated and demonstrated with the illustration of supplier selection problem. The merits and limitations of the proposed decision-making approach is also discussed.
A Variable-Indeterminacy Weighted Correlation Framework in Neutrosophic Statistics Prasanta Kumar Raut
Journal of Analytical Uncertainty Vol. 1 No. 1 (2025): JAU: December 2025
Publisher : Winaya Inspirasi Nusantara Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63924/jau.v1i1.264

Abstract

Correlation analysis under uncertain and imperfect information remains a fundamental challenge in statistical modeling. Although neutrosophic statistics provides a flexible framework for handling truth, falsity, and indeterminacy simultaneously, most existing neutrosophic correlation measures treat the indeterminacy component as uniform across observations. Such an assumption may oversimplify real data, where uncertainty often varies due to measurement errors, subjective assessments, or incomplete knowledge. In this paper, a weighted neutrosophic correlation framework with observation-dependent indeterminacy is developed. The proposed model allows each data point to possess its own indeterminacy level and incorporates weighting parameters to reflect the relative reliability or importance of observations. Based on these considerations, a new weighted neutrosophic correlation coefficient is formulated using neutrosophic means, variances, and covariance. Fundamental statistical properties of the proposed coefficient, including boundedness, symmetry, and invariance under linear transformations, are rigorously established. A detailed numerical illustration is provided to demonstrate the computational procedure and to show how variable indeterminacy and weighting influence the strength of association between variables. The proposed approach offers a more realistic and adaptable tool for analyzing relationships in uncertain environments and may serve as a useful foundation for further developments in neutrosophic data analysis and decision-making applications.
Multi-Criteria Decision Making for Smart City Implementation Using Fuzzy Soft Maut Method Adem Yolcu; Taha Yasin Öztürk
Journal of Analytical Uncertainty Vol. 1 No. 1 (2025): JAU: December 2025
Publisher : Winaya Inspirasi Nusantara Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63924/jau.v1i1.269

Abstract

Smart city development requires making choices based on multiple and competing criteria. Thus, in these kinds of environment, the opinions of experts are often given in terms of vague language rather than specific numbers. This paper investigates this challenge by developing a Fuzzy Soft Multi-Attribute Utility Theory (MAUT) system for assessing the readiness of metropolitan municipalities to implement a smart city. This is a hybrid method which integrates the parameter-based structured approach of soft sets with the ability to model uncertainty in fuzzy sets and usefulness aggregation rules of MAUT. Triangular fuzzy numbers are employed for the representation of an expert’s judgment. A procedure is followed in order to aggregate, normalize the assessments, and then combine them in an overall utility score. The model is developed to help decision-makers in creating a transparent and flexible method to address numerous criteria in uncertain situations in the context of urban and smart city planning.
Multiple-Attribute Decision-Making Based on AHP-TOPSIS for Gas Station Site Selection Problem Muhammad Nabil Maulana; Kikye Martiwi Sukiakhy; Rini Deviani; Sri Azizah Nazhifah; Husaini Husaini; Irvanizam Irvanizam
Journal of Analytical Uncertainty Vol. 1 No. 1 (2025): JAU: December 2025
Publisher : Winaya Inspirasi Nusantara Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63924/jau.v1i1.270

Abstract

The continuous growth of private vehicle usage in Indonesia has led to a significant increase in fuel demand, making the strategic placement of gas stations a critical issue for transportation infrastructure planning. However, inappropriate site selection may result in uneven service coverage, affecting increased operational costs and reduced accessibility for road users. Therefore, a systematic and objective decision-making approach is required to support gas station location planning. Motivated by this challenge, this study develops an integrated decision-support framework to evaluate and select strategic gas station sites based on multiple criteria. The framework combines the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The AHP method is employed in the first stage to determine the relative importance weights of the evaluation criteria based on expert judgments. In the second stage, the TOPSIS method is implemented to rank candidate locations and identify the alternative closest to the ideal solution. To validate the proposed framework, a case study involving multiple candidate locations is experimented with. Experimental results demonstrate that the proposed AHP–TOPSIS approach is a practical tool for selecting gas station site location, with location L3 identified as the most strategic site for gas station construction.
Role of Fermatean Neutrosophic Sets in Determination of Career Planning Mamoni Dhar
Journal of Analytical Uncertainty Vol. 1 No. 2 (2026): JAU: June 2026
Publisher : Winaya Inspirasi Nusantara Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63924/jau.v1i2.293

Abstract

Fermatean Neutrosophic sets that combine membership degrees of Pythagorean fuzzy sets and neutrosophic sets provide a more effective means of representing uncertainty, vagueness, and indeterminacy. These type of sets are commonly utilized across various disciplines which offers a structure for addressing intrinsic uncertainties that inherits in real life situations, for example, when making decisions about career options, pattern identification, work-life equilibrium and entrepreneurship. This study mainly examines Fermatean neutrosophic sets, the various distance measures of Fermatean neutrosophic sets and the associated properties. The characteristics of these features are also investigated. In a Fermatean neutrosophic environment, distance measures play a very important role. These are used extensively in pattern recognition, medical diagnosis and decision making. The literature on neutrosophic theory has offered a variety of distance measurements over time, each with specific benefits and drawbacks.  The common metrics used are Hamming, Euclidean and Hausdroff distances which are calculated element wise across the sets. Taking into consideration of the existing ambiguities, a new distance metric is introduced here for dealing with Fermatean neutrosophic sets. This method provides more comprehensive and reliable possibilities which can be helpful for prospective candidates to assess complex career options. Decision-making is aided by an approach described here for both people and organizations. Furthermore, an application based on Fermatean neutrosophic sets is shown to demonstrate the method's functionality.
Erdős–Rényi Models and Neutrosophic Sets Binod Kumar Sharma; Apostolos Syropoulos
Journal of Analytical Uncertainty Vol. 1 No. 2 (2026): JAU: June 2026
Publisher : Winaya Inspirasi Nusantara Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63924/jau.v1i2.308

Abstract

Networks comprise a vast array of diverse entities, including organizations, computers, airports, and more. The elements are interconnected either physically (e.g., through cables or microwaves) or mentally, when they pertain to ideas or concepts. In the broadest context, the linkages exhibit no discernible pattern. Graphs are mathematical constructs utilized to represent networks and analyze their characteristics. A random graph is one in which a stochastic process dictates the existence of an edge between two vertices. An Erdős–Rényi model is a framework for generating random graphs or examining the evolution of a random graph. Substituting likelihood with plausibility results in a fundamentally distinct framework that can be mathematically characterized using fuzzy graphs. In addition, we gain far more flexibility by adding a measure of the opposite of plausibility and a measure of indeterminacy. This naturally leads to a reinterpretation of established concepts and ideas and the framework of neutrosophic set theory.
Recursive Intuitionistic Fuzzy SuperHyperGraphs Takaaki Fujita
Journal of Analytical Uncertainty Vol. 1 No. 2 (2026): JAU: June 2026
Publisher : Winaya Inspirasi Nusantara Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63924/jau.v1i2.273

Abstract

Finite hypergraphs generalize ordinary graphs by allowing each hyperedge to connect an arbitrary nonempty subset of vertices, thereby providing a natural framework for genuinely multiway interactions. To represent hierarchical and multi-layer structures, SuperHyperGraphs further extend this framework via iterated powerset constructions, so that set-valued objects formed at one level may serve as vertices at higher levels. Independently, recursive hypergraphs enrich the edge structure by allowing a hyperedge to contain not only vertices but also lower-level hyperedges, yielding nested incidence relations under a prescribed recursion depth. In this paper, we unify these two directions and introduce Recursive Intuitionistic Fuzzy SuperHyper Graphs. The proposed model combines hierarchical supervertices, recursively defined superhyperedges, and intuitionistic fuzzy membership/non-membership grades in the sense of Atanassov, enabling the representation of higher-order systems that are simultaneously hierarchical, recursive, and uncertain. We formulate the structure on a well-founded recursive universe, establish the fundamental axioms (including vertex–edge consistency and covering conditions), and study basic structural properties. In particular, we discuss induced substructures, isomorphisms, and level-induced (depth-truncated) structures, and clarify how the model re duces to standard intuitionistic fuzzy hypergraph-type objects in special cases. The proposed framework provides a mathematically consistent foundation for modeling complex relational systems with nested inter actions and uncertainty across multiple levels of organization.
The Neutrosophic Weibull-Rayleigh Distribution: A Flexible Lifetime Model under Indeterminacy Adnan Amin; Mehran khan
Journal of Analytical Uncertainty Vol. 1 No. 2 (2026): JAU: June 2026
Publisher : Winaya Inspirasi Nusantara Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63924/jau.v1i2.290

Abstract

The classical Weibull-Rayleigh distribution is widely used in survival, reliability, and environmental studies. However, in many real-world applications, the model parameters are not precisely known due to measurement errors, incomplete data, or ambiguous information. Classical statistics cannot adequately handle such indeterminacy. This paper introduces the neutrosophic Weibull-Rayleigh distribution by extending the classical Weibull-Rayleigh distribution. We derive the neutrosophic probability density function, cumulative distribution function, survival function, hazard function, moments, moment generating function, and Shannon entropy. A simulation study demonstrates that as indeterminacy increases, all summary statistics become intervals rather than point estimates, with band widths that quantify parameter uncertainty. The proposed distribution is recommended for modeling lifetime data in medical diagnosis (incomplete patient records), environmental monitoring (sensor failures), and reliability engineering (imprecise component specifications) where indeterminacy is inherent.
Designing Bayesian Skip-Lot Sampling Plan V (Sksp–V) for Clinical Trial Monitoring Kavithamani M; Gopinath Malaisamy; Bharath N
Journal of Analytical Uncertainty Vol. 1 No. 2 (2026): JAU: June 2026
Publisher : Winaya Inspirasi Nusantara Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63924/jau.v1i2.291

Abstract

This study develops a Bayesian extension of the Skip-Lot Sampling Plan V (SkSP V) and demonstrates its utility in adaptive clinical trial monitoring. The proposed approach incorporates prior information, probabilistic decision rules, and a skipping mechanism to minimize patient exposure while preserving statistical efficiency. Methodologically, the plan defines operating characteristic (OC) curves, average outgoing quality (AOQ), average outgoing quality limit (AOQL), and expected sample number (EN) under a Bayesian framework. A case study in a Phase II clinical trial context illustrates the adaptability of SkSP V to real-world monitoring, where patient cohorts are treated as lots and treatment failures as defectives. Simulation results show that Bayesian SkSP V achieves higher acceptance probability at acceptable efficacy levels while maintaining strong protection against poor treatments at limiting efficacy levels. Compared with alternative skip-lot designs such as SkSP III, the Bayesian SkSP V achieves a lower AOQL and reduced expected sample number, thereby offering both ethical and statistical advantages. These findings suggest that Bayesian SkSP V provides a robust, efficient, and ethically favorable framework for modern clinical research, bridging the gap between industrial quality control methods and biomedical trial monitoring.

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