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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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Fuzzy Rules for Data Set Classification: A Hybrid Approach Using Rough Set and Grid Partitioning Daniachew, Adeola Azy; Clevon, Averey Barack; Avram, Abimelech Keita; Chislon, Dodavah Tesseman
International Journal of Enterprise Modelling Vol. 13 No. 3 (2019): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/emod.v13i3.73

Abstract

This research aims to address the issue of exponential rule generation in fuzzy rule-based classification systems by developing a hybrid grid partition and rough set method. Fuzzy rule-based classification systems have the potential to construct linguistically understandable models, but a major constraint is the significant increase in the number of rules with a high number of attributes, which can diminish interpretation and classification accuracy. In this study, the grid partition method is utilized to generate fuzzy rules with adaptively adjusted grid structures, thus avoiding exponential rule proliferation. The research encompasses the use of the Iris Flower dataset, rule formation while considering variable precision, and classification accuracy testing. The research findings indicate that the hybrid grid partition and rough set method produces more efficient and accurate fuzzy rules, with a classification accuracy rate of 83.33%. This method also successfully reduces the number of generated rules, making it a promising solution to tackle the issue of exponential rule increase in fuzzy rule-based classification systems. The conclusions of this research can be described based on the findings, discussions and results above are: The application of the rough set method at the beginning of rule formation can reduce the number of condition attributes and the number of redundant objects so that the rule formation process becomes more concise The grid partition method with a grid structure applying adapted techniques produces fuzzy rules that have the potential to be generated. The hybrid grid partition method and rough set method produce classification rules that do not increase exponentially. The number of classification rules generated decreases as the number of condition attributes and the number of objects classified decrease. Fuzzy rules generated by the hybrid method produce a classification accuracy rate of 83.3% with 9 data records and the number of unclassified data is 0.
Enhancing Multiplication Skills: The Way Modeling Method and Mathchess Games in Educational Practice Pujiastuti, Lise; Wahyudi, Mochamad
International Journal of Enterprise Modelling Vol. 17 No. 3 (2023): September: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/emod.v17i3.78

Abstract

This research delves into the exploration of an innovative educational approach aiming to enhance multiplication skills among students. The study investigates the combined efficacy of the Way Modeling Method, utilizing visual representations, and Mathchess games, a gamified learning approach, in improving multiplication proficiency. Through a quasi-experimental design involving a control and experimental group, elementary school students aged 8 to 10 were exposed to either traditional instruction or the combined intervention. Pre-tests and post-tests were administered to measure changes in multiplication skills, accompanied by qualitative assessments through participant feedback and observations. The results unveiled significant improvements in the experimental group, indicating a substantial enhancement in accuracy, comprehension, engagement, and confidence in solving multiplication problems. Comparative analysis between groups highlighted the distinct effectiveness of the combined methodology, aligning with cognitive learning theories and emphasizing the potential for dynamic and interactive pedagogical approaches in fostering mathematical skills. These findings present implications for educational practice, advocating for the integration of diverse teaching methodologies catering to varied learning styles. Furthermore, they pave the way for future research in optimizing these approaches and exploring their broader applications in mathematical education.
Enhancing Mathematics Education Through Collaborative Learning: A Study of Two Stay Two Stray (Ts-Ts) and Think-Pair-Share (TPS) Models within Realistic Mathematics Education Sunandar, Dede
International Journal of Enterprise Modelling Vol. 17 No. 3 (2023): September: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/emod.v17i3.79

Abstract

This study evaluates the Two Stay Two Stray (Ts-Ts) and Think-Pair-Share (TPS) learning models in Realistic Mathematics Education (PMR) arithmetic learning activities. The study examines how collaborative pedagogical methods affect students' mathematical ability, problem-solving skills, and participation in varied educational environments. A quasi-experimental approach included various participant groups of varying grade levels and academic skills. To evaluate results, the research methodology included quantitative assessments, qualitative observations, questionnaires, interviews, and data analysis. After PMR used Ts-Ts and TPS models, pupils' mathematics understanding improved significantly. Quantitative studies showed improved test results, indicating student proficiency. Qualitative evaluations showed higher engagement, dynamic peer interactions, and deeper conceptual understanding during collaborative learning. Student questionnaires and educator interviews confirmed that these models promote communication, critical thinking, and math applications. TPS highlighted systematic individual contemplation and inclusive involvement, while Ts-Ts promoted vibrant group conversations. The results imply that the Ts-Ts and TPS models work together to promote collaborative learning and accommodate varied learning styles. Integrating these concepts into curriculum frameworks, promoting educator professional development, and pushing for inclusive mathematical education policy are recommended.
Unveiling Agricultural Land Dynamics: Satellite-Based Change Detection for Sustainable Farming Practices Alesha, Aisyah; Lee , Ricardo
International Journal of Enterprise Modelling Vol. 17 No. 3 (2023): September: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/emod.v17i3.80

Abstract

The study investigates the intricate dynamics of agricultural landscapes through the lens of satellite imagery and remote sensing technologies. Leveraging multi-source data and advanced analytical techniques, the research aims to detect and analyze changes in agricultural land, spanning land use patterns, crop health, and environmental impacts. Using a combination of satellite imagery from diverse sources such as Landsat, Sentinel missions, and commercial providers, the research employs spectral analysis, machine learning algorithms, and temporal assessments to unveil temporal and spatial changes in agricultural terrains. The findings showcase significant shifts in land use, highlighting urban encroachment, alterations in crop patterns, and ecological impacts of agricultural practices. Insights into crop health indicators reveal stress factors affecting agricultural productivity, aiding in precision agriculture and adaptive farming strategies. Moreover, the research extends its implications beyond agriculture, influencing policy-making, environmental conservation efforts, and technological innovations. It serves as a foundation for sustainable land management, guiding policies and practices that harmonize agricultural productivity with ecological preservation
Enhancing Construction Worker Safety: Real-time Health Monitoring through Wearable Technology Lee, Poizot Li
International Journal of Enterprise Modelling Vol. 17 No. 3 (2023): September: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/emod.v17i3.81

Abstract

Wearable technology can improve worker safety, productivity, and well-being in the construction business, according to this study. Wearable device health parameters are analyzed quantitatively and worker experiences and perceptions are analyzed qualitatively in the study. A paradigm change in construction worker health monitoring is real-time monitoring of important health parameters including heart rate, body temperature, and hazardous exposure with wearable technology. Continuous monitoring allows workers and management to quickly identify and mitigate health risks, creating a safer workplace. Data shows wearable technology's many benefits. Over and above safety, the technology promotes health by giving workers individualized input to make informed health decisions. Management can optimize work conditions, allocate resources, and improve safety standards via data-driven decision-making. Technology adoption issues like privacy and user approval are important. Successful integration into the construction workplace requires ethical practices and user-centric design. This study shows that wearable technologies can transform construction worker health monitoring. The study's findings inspire innovation, interdisciplinary collaboration, and a concentrated effort to make construction workplaces safer, healthier, and more efficient.
Enhancing hotel investment analysis: A decision support system utilizing the Fuzzy Tsukamoto Method in the hospitality industry Rioles, Philiphe de; Kristomus, Kristomus
International Journal of Enterprise Modelling Vol. 17 No. 3 (2023): September: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/emod.v17i3.82

Abstract

This research uses the Fuzzy Tsukamoto Method to construct a decision support system (DSS) to change hotel investment decision-making. The study integrates quantitative and qualitative elements, accommodates uncertainties, and provides stakeholders with full insights for informed decision-making to meet the complexity of hotel investments. Systematic data collecting from financial measures, market trends, customer preferences, and expert opinions is used. The DSS combines various inputs using computer intelligence to analyze investment opportunities. The DSS's capacity to include subjective inputs and enable scenario evaluations shows its complete grasp of investments. Consumer behavior, market trends, risk assessments, and sustainability correlations greatly impact investment strategies. System strengths include adaptability, extensive analysis, and educated decision-making. However, interpretability, data quality, and real-time adaption issues recommend future study improvements. In hotel investment decision assistance, this research is groundbreaking. The DSS's sophisticated, data-driven insights can allow stakeholders to take a more strategic, sustainable, and adaptive approach to the changing hospitality business. Further refinements and innovations will strengthen its effectiveness, enabling robust and informed hotel investment decisions in the changing landscape
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.

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