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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 114 Documents
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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

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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.
Optimizing Educational Scheduling: ACO-Based Lecture Schedule Preparation Application Jhonlawi, Bonie Alkhan
International Journal of Enterprise Modelling Vol. 18 No. 2 (2024): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

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Abstract

This research endeavors to revolutionize educational scheduling by introducing a Lecture Schedule Preparation Application founded on Ant Colony Optimization (ACO) algorithms. The study addresses the intricate challenges inherent in scheduling courses within educational institutions. By leveraging ACO's adaptability and optimization capabilities, the application aims to efficiently allocate courses to rooms, time slots, and days while considering diverse constraints, faculty preferences, and institutional requirements. The investigation delves into the application's design, implementation, and performance in comparison to traditional heuristic approaches and other metaheuristic algorithms. Emphasizing adaptability and user-centricity, the application incorporates user preferences and institutional constraints, aligning schedules more closely with real-world needs. Key findings highlight the application's efficacy in optimizing resource utilization, enhancing scheduling efficiency, and accommodating real-time changes. The study underlines the significance of ACO's scalability, adaptability, and robustness in handling complex scheduling scenarios prevalent in educational settings. The research contributes to the realm of educational scheduling by introducing an innovative and adaptable solution. The findings underscore the transformative potential of ACO algorithms in streamlining scheduling processes, thereby fostering a more harmonious and efficient educational environment.
Enhancing Camera Purchasing Decisions: A Multicriteria Decision Support System using the Dempster-Shafer Method Winiski, Raditya
International Journal of Enterprise Modelling Vol. 18 No. 2 (2024): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

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Abstract

This research explores the development and implications of a Decision Support System (DSS) tailored for assisting consumers and businesses in navigating the complexities of camera purchasing decisions. Leveraging the Dempster-Shafer method, the study establishes a structured framework integrating multiple decision criteria to provide personalized recommendations aligned with user preferences. The research begins by delineating the significance of informed decision-making in camera purchases, acknowledging the intricate interplay between technical specifications, user preferences, and brand considerations. Building upon this premise, the study articulates the methodology employed in designing and implementing the DSS, emphasizing the integration of the Dempster-Shafer method to amalgamate beliefs across diverse criteria. Through a systematic approach, the DSS demonstrates strengths in its comprehensive multicriteria decision support, personalized recommendation generation, and robust handling of uncertainties inherent in decision-making.
Optimizing Football Tournament Predictions: A Decision Support System Utilizing the ELECTRE Method for Multi-Criteria Outcome Forecasting Simbolon, Gioriswan Frengky; Marcelino, Fiki
International Journal of Enterprise Modelling Vol. 18 No. 2 (2024): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

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Abstract

This research introduces a sophisticated Decision Support System (DSS) tailored for predicting football tournament outcomes, leveraging the robustness of the ELECTRE (Elimination and Choice Translating Reality) method. The study outlines a systematic methodology integrating multi-criteria decision-making principles, historical data, and diverse performance metrics to forecast match results. The research framework encompasses data collection, criteria identification, and the application of the ELECTRE method, assigning relative weights to criteria and establishing thresholds for evaluation. The DSS facilitates nuanced comparisons between teams across multiple criteria, navigating uncertainties and accommodating imprecise data inherent in sports analytics. Moreover, the research delineates the development of the predictive model, its calibration, and evaluation against historical data. Through a comparative analysis with other prediction methods, the study showcases the strengths of the ELECTRE-based DSS in providing comprehensive insights into football tournament outcomes. The implications of accurate predictions resonate across various stakeholders, influencing strategic decisions for teams, enhancing fan engagement, impacting betting landscapes, and shaping advertising and broadcasting strategies within the sports industry.
Enhancing Scholarship Recipient Selection: A Fuzzy Logic-Based Decision Support System Utilizing the Tsukamoto Method Rustanto, Herian Setya
International Journal of Enterprise Modelling Vol. 18 No. 2 (2024): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

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Abstract

Cholarship recipient selection stands as a critical yet challenging process, often constrained by traditional methods reliant on rigid criteria that struggle to encompass the diverse talents and achievements of applicants. This research introduces a novel Decision Support System (DSS) empowered by fuzzy logic and the Tsukamoto method to revolutionize this selection process. The DSS transcends the limitations of conventional methods by accommodating imprecise data, embracing a holistic evaluation framework that considers multifaceted criteria such as academic performance, extracurricular activities, and personal attributes. Leveraging fuzzy rules and linguistic variables, the system navigates uncertainty, fostering fairness, and transparency in decision-making. Rigorously tested for efficiency, accuracy, and reliability, the DSS emerges as a transformative tool, redefining scholarship selection paradigms. This research not only presents a cutting-edge system but also sets a precedent for advanced decision support systems, marking a shift towards more inclusive, adaptable, and precise evaluation methodologies.
Enhancing Cultural Resonance: A Decision Support System for Selecting Islamic Names using the Analytical Hierarchy Process Ahmad, Fauzan; Hanafiah, Adelia
International Journal of Enterprise Modelling Vol. 18 No. 2 (2024): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

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Abstract

The selection of Islamic names represents a deeply rooted cultural tradition intertwined with spiritual significance, historical resonance, and familial heritage within the Muslim community. This research endeavors to develop a Decision Support System (DSS) utilizing the Analytical Hierarchy Process (AHP) methodology to navigate the multifaceted process of choosing Islamic names. The study integrates quantitative analysis with cultural sensitivity to create a structured framework for evaluating names based on criteria encompassing meanings, cultural relevance, pronunciation, and familial connections. Expert opinions and user perspectives are incorporated to establish criteria weights, providing a systematic approach to prioritize attributes aligned with cultural traditions and personal preferences. The implementation of the DSS aims to empower individuals and families to make informed, culturally resonant choices while fostering transparency, reducing subjective biases, and enhancing understanding of the profound significance inherent in Islamic names. Furthermore, the research acknowledges the adaptive nature of the DSS, capable of accommodating evolving cultural norms and diverse perspectives within the Muslim community. Ultimately, this study seeks to bridge the gap between tradition and modern decision-making methodologies, fostering a deeper appreciation for the rich heritage encapsulated within the selection of Islamic names.
Enhancing Employee Performance Evaluation: A Decision Support System Utilizing Analytical Hierarchy Process for Fair Bonus Allocation Wibisono, Mohamad Bayu; Wahyono, Bambang Tri; Solihin, Indra Permana; Wirawan, Rio
International Journal of Enterprise Modelling Vol. 18 No. 3 (2024): September: Enterprise Modelling
Publisher : International Enterprise Integration Association

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Abstract

This research endeavors to revolutionize the process of employee performance evaluation and bonus allocation within organizational settings by introducing a sophisticated Decision Support System (DSS) underpinned by the Analytical Hierarchy Process (AHP). The study delves into the development, implementation, and testing phases of the DSS, aiming to enhance objectivity, fairness, and efficiency in decision-making methodologies. The research commences with an exploration of existing challenges in performance evaluation systems, acknowledging the subjectivity and limitations prevalent in traditional methods. The conceptual framework outlines the hierarchical structure of the DSS, encompassing diverse performance criteria and sub-criteria essential for a comprehensive evaluation. Implementation involves the integration of the AHP method into the DSS, facilitating precise pairwise comparisons, priority vector calculations, and weighted score determinations. Rigorous testing and validation phases ascertain the system's accuracy, consistency, and responsiveness in evaluating employee performance and aligning bonus allocation with contributions. Results from the testing phase illuminate the DSS's efficacy, showcasing its ability to provide transparent and data-driven evaluations, fostering fairness, trust, and intrinsic motivation among employees. The implications of employing this DSS extend beyond bonus allocation, influencing organizational performance, decision-making, and the broader organizational climate.
Optimizing Crop Selection: A Multi-Criteria Decision Support System for Sustainable Agriculture Gunawan, Muhammad Indra; Sitopu, Joni Wilson; Sechan, Gamar; Gunawan, Indra
International Journal of Enterprise Modelling Vol. 18 No. 3 (2024): September: Enterprise Modelling
Publisher : International Enterprise Integration Association

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Abstract

This research endeavors to revolutionize agricultural decision-making through the development and application of a robust Decision Support System (DSS) employing the Multi-Criteria Decision Making (MCDM) method. Recognizing the complexities inherent in crop selection, the study aims to bridge the gap between traditional manual methodologies and the need for a more comprehensive, objective, and data-driven approach. The research foundation rests on the understanding that crop selection is a multifaceted process influenced by diverse and interrelated factors. Leveraging technology and structured methodologies, the developed DSS offers a systematic and holistic evaluation of potential crops by integrating various criteria such as climate suitability, market demand, soil fertility, and sustainability metrics. The system's ability to consider multiple criteria simultaneously surpasses conventional single-factor approaches, providing stakeholders with a nuanced and comprehensive perspective. While demonstrating strengths in comprehensive evaluation and objectivity, the research also identifies areas for improvement. The dependency on data quality and quantity emerges as a limitation, urging the need for enhanced data sourcing and refinement. Additionally, further development in handling intricate trade-offs and improving user accessibility could bolster the system's applicability and acceptance within agricultural practices. The practical implications of this research reverberate across the agricultural domain.
Optimizing Vermicelli Production: Flour Evaluation Using WASPAM Methodology for Informed Decision-Making in the Food Industry Immanuela, Noviany
International Journal of Enterprise Modelling Vol. 18 No. 3 (2024): September: Enterprise Modelling
Publisher : International Enterprise Integration Association

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Abstract

This research employs the Weighted Aggregated Sum Product Assessment Method (WASPAM) to evaluate and determine the most suitable flour for vermicelli production. The study aims to streamline the complex decision-making process inherent in flour selection by considering crucial criteria texture, gluten content, and protein levels to assess multiple flour options comprehensively. The assessment highlights texture as a pivotal criterion significantly influencing the suitability of flour for vermicelli production. Flour samples excelling in texture consistently ranked higher, emphasizing its paramount importance in achieving desired consistency and mouthfeel in vermicelli noodles. Additionally, while gluten content and protein levels played substantial roles, a balanced performance across criteria often resulted in competitive suitability scores, emphasizing the necessity of a holistic approach in flour selection. The findings offer valuable insights for stakeholders in the food industry, providing guidance for optimal flour selection strategies aligned with quality preferences and market demands. The research recommends a refined approach to weight allocation, particularly considering the pronounced influence of texture.
Forecasting Outpatient Visits: Leveraging Genetic Fuzzy Systems for Enhanced Healthcare Management at Efarina Etaham Berastagi Hospital Aveno, Dicky Chandra
International Journal of Enterprise Modelling Vol. 18 No. 3 (2024): September: Enterprise Modelling
Publisher : International Enterprise Integration Association

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Abstract

The efficient management of patient influx within healthcare facilities poses a critical challenge, necessitating precise forecasting and resource allocation. This study explores the predictive modeling of outpatient visits at Efarina Etaham Berastagi Hospital employing the innovative Genetic Fuzzy Systems (GFS) method. Harnessing the synergy between genetic algorithms and fuzzy logic, this research endeavors to develop a predictive model capable of accurately anticipating the fluctuating patterns of outpatient visits. The study amalgamates historical visit records, patient demographic data, and temporal factors within the GFS framework, aiming to optimize resource allocation, refine scheduling strategies, and elevate patient care delivery. The methodology involves the integration of genetic algorithms to iteratively evolve the predictive model and fuzzy logic to handle uncertainties inherent in healthcare datasets. The model's performance is evaluated through rigorous analysis, validation against actual visitation data, and comparison against established metrics to ascertain its accuracy and reliability. The outcomes of this research unveil a predictive model capable of forecasting outpatient visits with notable accuracy, showcasing the potential of the GFS method in enhancing healthcare management.

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