cover
Article Per Year (5 Year)
All
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
International Journal of Enterprise Modelling
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 5 Documents
 1 
Search results for , issue "Vol. 18 No. 1 (2024): Jan: Enterprise Modelling" : 5 Documents clear
Optimizing Pricing Strategies: Integrating Dempster-Shafer Method in Decision Support Systems for Uncertainty Management Feriantomi, Rey; Heksana, Syaifa
International Journal of Enterprise Modelling Vol. 18 No. 1 (2024): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

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

Abstract

This research explores the integration of the Dempster-Shafer method within decision support systems to revolutionize pricing strategies in dynamic business environments. The study investigates the method's efficacy in managing uncertainties, synthesizing diverse evidence sources, and fostering informed decision-making in pricing scenarios. Through a structured approach, the Dempster-Shafer method enables decision-makers to navigate uncertainties, integrate multifaceted evidence, and refine pricing strategies with greater precision and adaptability. Findings showcase its transformative potential, offering insights into risk management, holistic integration of information, structured conflict resolution, and agile responsiveness to market dynamics. While demonstrating significant contributions, challenges in computational complexity, interpretability, and integration emerge, presenting avenues for further research and refinement. This research signifies a paradigm shift, emphasizing the importance of innovative computational methodologies in empowering evidence-based, proactive decision-making in pricing strategies across industries.
Optimizing Supplier Selection: Leveraging Analytic Hierarchy Process (AHP) in Purchasing Decision Support Systems Hamizahrul, Fadhilatul Yusuf
International Journal of Enterprise Modelling Vol. 18 No. 1 (2024): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

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

Abstract

This research delves into the application and significance of the Analytic Hierarchy Process (AHP) in the domain of purchasing decisions, focusing on the structured methodology's impact on decision-making processes. The study seeks to explore how AHP serves as a robust decision support system, aiding in supplier selection, criteria prioritization, and overall procurement strategies. The research methodology involves a comprehensive analysis of AHP's implementation in the context of purchasing decisions. It encompasses a systematic review of literature, data collection methods including surveys and interviews with stakeholders, and the application of AHP models in evaluating and ranking suppliers based on identified criteria. Key findings underscore the pivotal role of AHP in structuring complex decisions by breaking them down into hierarchical structures. The research highlights AHP's ability to quantify criteria importance, integrate diverse stakeholder preferences, and prioritize suppliers based on overall performance across critical factors such as cost, quality, and delivery time. The outcomes of this study showcase the significant impact of AHP on enhancing decision quality, transparency, and resource optimization in purchasing decisions. The findings emphasize the methodology's adaptability to changing contexts and its role in fostering continuous improvement, aligning choices with organizational objectives, and mitigating risks associated with supplier selection.
Enhancing Car Purchasing Decisions: A Simple Additive Weighting-Based Decision Support System for Multi-criteria Evaluation Abyakta, Dimas Eri
International Journal of Enterprise Modelling Vol. 18 No. 1 (2024): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

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

Abstract

This research focuses on the development and implementation of a Car Purchasing Decision Support System, employing the Simple Additive Weighting (SAW) method, aimed at aiding consumers in navigating the multifaceted landscape of car purchases. The system utilizes a structured approach to evaluate and rank car models based on multiple criteria, including price, fuel efficiency, safety rating, and design preferences. Through the application of the SAW method, the research delineates a systematic framework for decision-making, offering transparency and clarity in the evaluation process. Findings highlight the effectiveness of the Decision Support System in providing structured guidance to buyers, empowering them with comprehensive information to make informed decisions aligned with their preferences. The study not only presents a hierarchy of suitability among car models but also emphasizes the significance of criteria weights in influencing the rankings. It underscores the system's adaptability, allowing for adjustments in criteria weights to accommodate changing buyer preferences and market dynamics. The implications of this research extend beyond individual decision-making, offering insights for industry stakeholders into consumer preferences and market trends. Recommendations for future improvements advocate for enhanced data integration, user-centric design, and the incorporation of ethical and social factors to further refine these Decision Support Systems.
Optimizing Manufacturing Operations: A Fuzzy Associative Memory-Based Decision Support System for Production Quantity Determination Bratadikara, Danendra Putra
International Journal of Enterprise Modelling Vol. 18 No. 1 (2024): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

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

Abstract

This research introduces a novel approach to optimizing production quantity determinations within manufacturing through the integration of a Decision Support System (DSS) based on the Fuzzy Associative Memory (FAM) method. The study explores the application of fuzzy logic principles and linguistic variables to enhance decision-making accuracy and efficiency in dynamic production environments. Leveraging the adaptability and robustness of FAM, the developed DSS accommodates uncertainty, complexity, and varied input parameters, offering nuanced and agile decision support capabilities. The research methodology involves defining linguistic variables for demand, resource availability, and production quantity, along with designing fuzzy sets and membership functions. The FAM model integrates these linguistic variables with IF-THEN fuzzy rules, capturing the intricate relationships between inputs and outputs. The DSS architecture incorporates this model, providing decision-makers with an intuitive interface for visualizing, analyzing, and selecting optimal production quantities. Results demonstrate the superiority of the FAM-based DSS over traditional methods, showcasing enhanced accuracy in production quantity estimations, efficient resource utilization, and agile responsiveness to changing demand scenarios. The system's adaptability and robustness contribute to mitigating risks associated with overproduction or underproduction, thereby optimizing inventory levels and reducing operational costs.
Enhancing Toddler Health Management: A Fuzzy Mamdani Decision Support System in Pediatric Healthcare Frenda, M. Iqbal; Azka, Rivani
International Journal of Enterprise Modelling Vol. 18 No. 1 (2024): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

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

Abstract

This research endeavors to develop a sophisticated Decision Support System (DSS) employing Fuzzy Mamdani reasoning tailored for toddler health management. Utilizing fuzzy logic principles, the system aims to revolutionize pediatric healthcare practices by offering precision, personalized care, and informed decision-making support. The DSS integrates linguistic variables, fuzzy sets, and Mamdani-type fuzzy reasoning to navigate the complexities of toddler health. By accommodating imprecise data, it provides nuanced assessments, enabling caregivers and healthcare professionals to make informed decisions regarding health concerns. Throughout the research, the system demonstrates strengths in precision assessments and personalized recommendations, enhancing its relevance in caregiving and healthcare decision-making. However, challenges in interpretability, data dependency, and implementation complexities surfaced, prompting the need for ongoing refinement and validation against clinical expertise. The implications of this research extend to real-world applications encompassing clinical settings, home healthcare, public health initiatives, and healthcare education. It signifies a significant stride towards transforming toddler healthcare, fostering better health outcomes and well-being for our youngest population.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 20 No. 1 (2026): January: Publishing Process (Inpress) 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