Garuda - Garba Rujukan Digital

All

International Journal of Enterprise Modelling

ieia
Website

Published by International Enterprise Integration Association

ISSN : 16939220 EISSN : 29878713 DOI : https://doi.org/10.35335/emod

Core Subject : Economy, Science, Education, Engineering,

Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

The International Journal of Enterprise Modelling serves as a venue for anyone interested in business and management modelling. It investigates the conceptual forerunners and theoretical underpinnings that lead to research modelling procedures that inform research and practice.

Arjuna Subject : Matematika - Matematika Terapan

Articles 109 Documents

1 2 3 4 5

Hybrid Grid Partition and Rough Set Method for Generation of Fuzzy Rules in Dataset Classification Park Vrançoisee Pernadate
International Journal of Enterprise Modelling Vol. 13 No. 1 (2019): January: Fuzzy Rule Generation
Publisher : International Enterprise Integration Association

The Hybrid Grid Partition and Rough Set Method for Fuzzy Rule Generation in Dataset Classification is a novel approach aimed at addressing the challenges of classifying datasets with uncertainty and imprecision. This methodology combines the concepts of grid partitioning, rough set theory, and fuzzy rule generation to enhance classification accuracy and interpretability. The hybrid grid partitioning technique divides the attribute space into a grid structure, capturing the underlying structure and relationships in the dataset. Rough set theory is then utilized to analyze the dataset and identify relevant attributes, reducing dimensionality and improving classification efficiency. Fuzzy rule generation employs fuzzy logic to capture imprecise and uncertain knowledge present in the dataset, generating flexible and robust fuzzy rules. Rule evaluation and selection processes are employed to identify high-quality rules for accurate and interpretable classification models. The proposed methodology offers a comprehensive framework for handling complex datasets, demonstrating improved classification performance in various domains. Experimental evaluations and comparisons with other classification approaches validate the effectiveness and practicality of the Hybrid Grid Partition and Rough Set Method for Fuzzy Rule Generation in Dataset Classification. This research contributes to advancing the field of dataset classification, particularly in scenarios where uncertainty and imprecision are prevalent. The proposed approach offers a comprehensive framework for handling complex datasets and improving classification performance in various domains.

Hybrid Grid Partition and Rough Set Method for Fuzzy Rule Generation in Dataset Classification: Enhancing Accuracy and Interpretability Norquist da Silva; Gregor Bogard Hohpe
International Journal of Enterprise Modelling Vol. 13 No. 1 (2019): January: Fuzzy Rule Generation
Publisher : International Enterprise Integration Association

This research presents a hybrid grid partition and rough set method for fuzzy rule generation in dataset classification, aiming to enhance accuracy and interpretability. The proposed mathematical model combines grid partitioning, rough set theory, and fuzzy logic to identify relevant attributes, reduce dimensionality, and generate interpretable fuzzy rules. The model is evaluated using a case example of iris flower classification and demonstrates competitive accuracy in predicting the species of iris flowers based on their attributes. The interpretability of the generated fuzzy rules provides transparent explanations for the classification decisions, allowing domain experts to understand and interpret the reasoning behind the predictions. Comparative analysis with traditional algorithms showcases the superiority of the hybrid model in terms of accuracy and interpretability. Sensitivity analysis enables parameter tuning and customization, further improving the model's performance. The practical implications of the hybrid model are discussed, and its potential applications in various domains are highlighted. The research concludes that the hybrid grid partition and rough set method offer an effective approach for accurate and interpretable dataset classification, with implications for decision-making and insights in real-world applications.

Enhancing Accuracy and Interpretability in Dataset Classification: Advancements in Hybrid Grid Partition and Rough Set Methods for Fuzzy Rule Generation Josea Moreno Chawla; Herrera Rocío
International Journal of Enterprise Modelling Vol. 13 No. 1 (2019): January: Fuzzy Rule Generation
Publisher : International Enterprise Integration Association

Accurate and interpretable classification of datasets plays a crucial role in various domains, including healthcare, finance, and image recognition. This research focuses on enhancing accuracy and interpretability in dataset classification through the integration of hybrid grid partition and rough set methods for fuzzy rule generation. The proposed mathematical model leverages the grid partition approach to handle the curse of dimensionality and reduce dataset complexity, while the rough set method identifies essential features and generates meaningful fuzzy rules. The assigned membership values to linguistic terms further enhance interpretability. The model's accuracy and interpretability were evaluated using a diabetes dataset, achieving an accuracy rate of 85% on the validation dataset and 83% on the testing dataset. Comparative analysis demonstrated competitive performance against existing methods. The iterative refinement process contributed to the model's optimization. However, limitations include dataset dependency, parameter sensitivity, and scalability. Future research directions include advanced rule pruning techniques, optimization of model parameters, handling imbalanced datasets, incorporating feature selection, robustness and scalability evaluation, comparative studies, and real-world application validation. The proposed model presents a promising approach to enhance accuracy and interpretability in dataset classification.

Grid Partitioning And Rough Set Method Approach For Fuzzy Rule Generation Chris Kornelisius; Eyvan Caeyso; Ching-Ghiang Feh
International Journal of Enterprise Modelling Vol. 13 No. 1 (2019): January: Fuzzy Rule Generation
Publisher : International Enterprise Integration Association

The generation of accurate and interpretable fuzzy rules plays a crucial role in various data analysis and decision-making systems. In this research, we propose a mathematical model based on grid partitioning and the rough set method for fuzzy rule generation. The model combines the advantages of grid partitioning, which enables localized analysis, and the rough set method, which captures the uncertainty in the dataset. By partitioning the input space into grids and determining the lower and upper approximations within each grid, the model generates accurate and representative fuzzy rules. These rules provide meaningful insights into the relationships between input variables and output variables, enhancing interpretability. The model is applied in a case example of temperature control to demonstrate its effectiveness. Additionally, a numerical example showcases the predictive performance and applicability of the model. The limitations of the research, such as dependency on data quality and scalability issues, are also discussed. Despite these limitations, the mathematical model contributes to the field of data analysis and decision-making systems by offering an approach that integrates grid partitioning and rough set method for fuzzy rule generation. It holds promise for applications in various domains, providing accurate and interpretable fuzzy rules for decision support systems and intelligent automation.

Optimizing Fuzzy Rule Generation: A Grid Partitioning and Rough Set Method Approach for Enhanced Accuracy and Interpretability Aisyah Alesha
International Journal of Enterprise Modelling Vol. 13 No. 1 (2019): January: Fuzzy Rule Generation
Publisher : International Enterprise Integration Association

This research focuses on optimizing fuzzy rule generation through the application of grid partitioning and rough set method, with the aim of enhancing both accuracy and interpretability. The proposed mathematical model addresses the challenge of generating accurate and interpretable fuzzy rule sets, particularly in the context of credit risk assessment. By utilizing grid partitioning, the input space is divided into regions, while the rough set method is employed to identify relevant features. The results show improved accuracy in classifying loan applicants into low-risk and high-risk categories, accompanied by enhanced interpretability through the generation of clear and understandable rules. The model's applicability extends to credit risk assessment and offers potential for further refinement and research. However, it is crucial to consider certain limitations, including the generalizability of results, sensitivity to grid partitioning, and the trade-off between accuracy and interpretability. In conclusion, the proposed model exhibits promise in generating accurate and interpretable fuzzy rule sets, thereby contributing to effective decision-making processes across diverse domains.

Exploring the Impact of Artificial Intelligence on Enterprise Modeling Denis Denunciar Otros; Vistos Otras
International Journal of Enterprise Modelling Vol. 13 No. 2 (2019): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research investigates the impact of artificial intelligence (AI) on enterprise modeling, with a specific focus on supply chain network design. The objective is to explore how AI techniques can enhance decision-making, improve efficiency, and drive cost reduction in enterprise modeling processes. The research utilizes case examples and numerical simulations to demonstrate the benefits and implications of incorporating AI techniques in enterprise modeling. The findings reveal that AI-enabled approaches in supply chain network design lead to cost reduction, improved customer service levels, accuracy improvement, efficiency gains, enhanced decision-making, and collaboration facilitation. The research highlights the importance of data availability, ethical considerations, organizational readiness, and interoperability in realizing the full potential of AI-enabled enterprise modeling. However, the research acknowledges the limitations, such as simplified examples and the specific context of supply chain network design. Future research is needed to validate the findings in diverse industry settings and address challenges related to data availability, ethical considerations, organizational readiness, and interoperability. This research contributes to the understanding of the positive impact of AI on enterprise modeling, providing valuable insights for organizations seeking to leverage AI techniques to optimize their decision-making processes and drive operational improvements.

Enhancing Decision-Making in Enterprise Modeling: A Comparative Analysis of Artificial Intelligence Techniques in Supply Chain Network Design Fianarantsoa Rakotoarisoa; Tanana Matsiatra
International Journal of Enterprise Modelling Vol. 13 No. 2 (2019): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research focuses on enhancing decision-making in enterprise modeling through a comparative analysis of artificial intelligence (AI) techniques in supply chain network design. The objective is to provide decision-makers with insights into the application, performance, and implications of different AI techniques in this domain. The research conducts a comprehensive comparative analysis of AI techniques, including machine learning algorithms, optimization algorithms, and expert systems. Performance evaluation metrics such as computational efficiency, accuracy, scalability, interpretability, and adaptability are established to assess the performance of these techniques. Real-world case studies are also presented to showcase the practical implementation and impact of AI techniques in supply chain network design. The findings contribute to informed decision-making by guiding decision-makers in selecting and implementing appropriate AI techniques. The research also identifies future research directions, including hybrid approaches, dynamic environment considerations, and the integration of AI with big data and the Internet of Things. Overall, this research provides valuable insights and guidelines for leveraging AI in supply chain network design, enabling decision-makers to optimize facility location, transportation routing, and inventory management, leading to improved operational efficiency and customer satisfaction.

Optimizing Supply Chain Network Design through Hybrid Artificial Intelligence Approaches: A Comparative Study Fernández Turay; Azzolini Matthew
International Journal of Enterprise Modelling Vol. 13 No. 2 (2019): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Supply chain network design plays a critical role in achieving operational efficiency and cost optimization. This research focuses on optimizing supply chain network design through the use of hybrid artificial intelligence (AI) approaches and presents a comparative study of different methods. The objective is to evaluate the effectiveness of combining machine learning, optimization algorithms, and expert systems in enhancing the design of supply chain networks. The research begins by formulating a mathematical model that captures the key decision variables, objectives, and constraints associated with supply chain network design. The model aims to minimize overall costs while considering factors such as facility selection, transportation routing, and product flows. To evaluate the performance of the hybrid AI approaches, various methods are compared, including genetic algorithms, particle swarm optimization, and reinforcement learning. Through extensive testing and analysis, the comparative study assesses the strengths and weaknesses of each approach in terms of solution quality, computational efficiency, and robustness. The study also considers the scalability of the methods to handle large-scale supply chain networks. The findings of the research demonstrate the benefits of integrating hybrid AI approaches in supply chain network design optimization. The hybrid AI methods outperform traditional optimization techniques, providing more accurate and efficient solutions. The comparative analysis highlights the specific scenarios in which each method excels, aiding decision-makers in selecting the most appropriate approach for their supply chain network design challenges. The research identifies opportunities for further enhancements and advancements in the field. Future research directions may include incorporating real-time data, considering uncertainty and risk factors, and extending the analysis to industry-specific applications. This research contributes to the optimization of supply chain network design by leveraging the power of hybrid AI approaches. The findings provide valuable insights for supply chain managers and decision-makers, enabling them to make informed choices that enhance operational efficiency, reduce costs, and improve overall supply chain performance.

Enhancing Supply Chain Network Design: Integration of Hybrid Artificial Intelligence and Real-Time Data for Dynamic Optimization Ahderom Rengga
International Journal of Enterprise Modelling Vol. 13 No. 2 (2019): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research focuses on enhancing supply chain network design through the integration of hybrid artificial intelligence (AI) and real-time data for dynamic optimization. The objective is to develop a mathematical formulation and model that minimize costs while meeting demand and capacity requirements. The research proposes the integration of hybrid AI techniques, such as machine learning and optimization algorithms, with real-time data to enable data-driven decision-making and adaptability to changing market conditions. The implementation involves collecting and processing real-time data from various sources and utilizing AI algorithms to optimize facility locations, transportation routes, and inventory allocation. A numerical example demonstrates the application of the model, showcasing cost savings and improved customer service. However, the research has limitations, including simplified assumptions, data quality concerns, scalability challenges, and the limited scope of considered factors. Despite these limitations, the findings highlight the potential benefits of integrating hybrid AI and real-time data in supply chain network design, offering insights for practitioners and future research directions.

Optimizing Sustainable Supply Chain Network Design using Hybrid AI and Real-Time Data Mocombe Celucien; Eécoles Notre
International Journal of Enterprise Modelling Vol. 13 No. 2 (2019): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research focuses on optimizing sustainable supply chain network design by leveraging hybrid AI techniques and real-time data integration. The objective is to minimize costs while considering carbon emissions, transportation modes, supplier selection, and inventory allocation. The research proposes a mathematical formulation model that incorporates these variables and constraints, enabling companies to make data-driven decisions and enhance their sustainability performance. Real-time data from various sources, including suppliers, transportation providers, and inventory systems, is collected and processed using AI techniques. The model is then solved using advanced optimization algorithms to determine the optimal supply chain network design. Sensitivity analysis is conducted to assess the robustness of the model and evaluate the impact of changing parameters and constraints. A case example illustrates the practical application of the research findings, highlighting the benefits of the hybrid AI and real-time data approach in achieving cost efficiency and sustainability goals. The research contributes to the field of supply chain management by providing insights into the integration of real-time data, AI techniques, and sustainability considerations in supply chain network design. It also identifies limitations and suggests areas for future research to enhance the applicability and scalability of the proposed approach.

Page 1 of 11 | Total Record : 109

1 2 3 4 5

Filter by Year

2019 2025

Filter By Issues

All Issue Vol. 19 No. 3 (2025): September: Enterprise Modelling Vol. 19 No. 2 (2025): May: Enterprise Modelling Vol. 19 No. 1 (2025): January: Enterprise Modelling Vol. 18 No. 3 (2024): September: Enterprise Modelling Vol. 18 No. 2 (2024): May: Enterprise Modelling Vol. 18 No. 1 (2024): Jan: Enterprise Modelling Vol. 17 No. 3 (2023): September: Enterprise Modelling Vol. 17 No. 2 (2023): May: Enterprise Modelling Vol. 17 No. 1 (2023): Jan: Enterprise Modelling Vol. 16 No. 3 (2022): Sep: Enterprise Modelling Vol. 16 No. 2 (2022): May: Enterprise Modelling Vol. 16 No. 1 (2022): Jan: Enterprise Modelling Vol. 15 No. 3 (2021): Sep: Enterprise Modelling: Quantum computing Vol. 15 No. 2 (2021): May: Enterprise Modelling Vol. 15 No. 1 (2021): Jan: Enterprise Modelling Vol. 14 No. 3 (2020): Sep: Enterprise Modelling Vol. 14 No. 2 (2020): May: Enterprise Modelling Vol. 14 No. 1 (2020): Jan: Enterprise Modelling Vol. 13 No. 3 (2019): Sep: Enterprise Modelling Vol. 13 No. 2 (2019): May: Enterprise Modelling Vol. 13 No. 1 (2019): January: Fuzzy Rule Generation More Issue

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Zeehimin Huang Ping

Contact Email
internationalenterpriseintegra@gmail.com

Phone
+6281360000791

Journal Mail Official
internationalenterpriseintegra@gmail.com

Editorial Address
Jl. Raya Abepura, Wahno, Kec. Abepura, Kota Jayapura, Papua 99926, Indonesia

Location
Kota jayapura,

P a p u a

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Zeehimin Huang Ping

Contact Email internationalenterpriseintegra@gmail.com

Phone +6281360000791

Journal Mail Official internationalenterpriseintegra@gmail.com

Editorial Address Jl. Raya Abepura, Wahno, Kec. Abepura, Kota Jayapura, Papua 99926, Indonesia

Location Kota jayapura, P a p u a INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Zeehimin Huang Ping

Contact Email
internationalenterpriseintegra@gmail.com

Phone
+6281360000791

Journal Mail Official
internationalenterpriseintegra@gmail.com

Editorial Address
Jl. Raya Abepura, Wahno, Kec. Abepura, Kota Jayapura, Papua 99926, Indonesia

Location
Kota jayapura,

P a p u a

INDONESIA