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All Journal Jurnal Teknik Informatika C.I.T. Medicom Journal of Computer Science and Research International Journal of Enterprise Modelling

Aisyah Alesha

Tajik State University of Commerce, Tajikistan

Author-ID : 6262537

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

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Articles

Integration of stochastic and robust optimization techniques into DEA model for more accurate and reliable efficiency estimation Hengki Tamando Sihotang; Patricius Michaud Felix; Aisyah Alesha; Joan De Mathew
Jurnal Teknik Informatika C.I.T Medicom Vol 14 No 2 (2022): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol14.2022.229.pp1-4

Efficiency assessment is vital to assessing decision-making units (DMUs) in numerous sectors. DEA is a prominent non-parametric efficiency assessment tool. Traditional DEA models assume deterministic inputs and outputs, ignoring real-world uncertainties and variability. To improve efficiency estimation, stochastic and robust optimization approaches can be integrated into DEA models. To improve efficiency estimation, we present a stochastic and robust optimization framework incorporating DEA. Probabilistic inputs and outputs allow stochastic optimization to account for uncertainty. The model can capture data variability and create stochastic DMU efficiency scores by adding probability distributions. For data uncertainties and outliers, the DEA model uses robust optimization. Robust optimization considers worst-case scenarios and minimizes extreme observations on efficiency estimation. This makes efficiency scores more resilient to data outliers and noise. DEA models benefit from stochastic and resilient optimization. First, considering data uncertainties and fluctuations improves DMU efficiency representation. Second, eliminating outliers and extreme observations improves efficiency estimation. Third, efficiency scores help decision-makers make better, more informed choices. A case study in a specific industry shows the framework's efficacy. We compare classic and integrated stochastic-robust DEA model outcomes. The integrated model provides more accurate and dependable efficiency estimates, helping decision-makers understand DMU performance. DEA models with stochastic and resilient optimization increase efficiency estimation. By considering uncertainties and outliers, this paradigm helps decision-makers evaluate DMUs in many sectors more accurately and reliably.

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.

Quantum computing for production planning Fristi Riandari; Aisyah Alesha; Hengki Tamando Sihotang
International Journal of Enterprise Modelling Vol. 15 No. 3 (2021): Sep: Enterprise Modelling: Quantum computing
Publisher : International Enterprise Integration Association

This research investigates the potential of quantum computing in production planning and addresses the limitations of conventional computing approaches. Traditional methods have been partially effective, but they struggle to solve complex optimization problems, accurately predict demand, and manage supply chains efficiently. The unique computational capabilities of quantum computing offer promising solutions to surmount these obstacles and revolutionize production planning processes. This study seeks to bridge the gap between quantum computing and production planning by analyzing the benefits, limitations, and challenges of its applicability in this field. It proposes customized algorithms and methodologies for leveraging quantum computation to enhance production planning efficiency, cost reduction, and decision-making processes. The research demonstrates the potential of quantum algorithms to minimize total production costs while appeasing demand and resource constraints through a numerical example and mathematical formulation. The results emphasize the advantages of quantum computing in terms of cost reduction, enhanced efficiency, and scalability. Comparisons with conventional methods illuminate the benefits and drawbacks of quantum computing in production planning. This research contributes to the development of novel strategies to improve production planning efficiency, lower costs, and enhance decision-making processes, allowing organizations to leverage quantum computing for optimized production operations

A hybrid approach for adaptive fuzzy network partitioning and rule generation using rough set theory: Improving data-driven decision making through accurate and interpretable rules Jonhariono Sihotang; Aisyah Alesha; Juliana Batubara; Sonya Enjelina Gorat; Firta Sari Panjaitan
International Journal of Enterprise Modelling Vol. 16 No. 1 (2022): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Data-driven decision making is vital in credit risk assessment and other areas. Complex datasets are hard to rule. We use adaptive fuzzy network partitioning, rough set theory, and rule generation to improve data-driven credit risk assessment. An adaptive fuzzy network partitioning algorithm is used to cluster the dataset. Each cluster instance receives fuzzy membership degrees. Next, rough set-based attribute reduction identifies credit risk assessment attributes inside each cluster. Finally, attributes are used to build accurate and understandable credit risk assessment criteria. A loan application dataset is used to test the suggested method. The results show successful loan application clustering and the creation of credit risk criteria for each cluster. Accurate predictions and interpretable rules improve credit risk assessment comprehension and decision-making. By merging adaptive fuzzy network partitioning, rough set theory, and rule generation, the hybrid methodology overcomes classic technique constraints. These methods create a comprehensive framework for credit risk assessment criteria that improves accuracy and interpretability. Financial institutions and credit providers may benefit from the approach. The proposed approach can be tested in multiple domains and extended to handle increasingly complicated datasets. Evaluating the methodology on real-world datasets and comparing it to existing methods can also reveal its practicality and efficacy. This research generates accurate and interpretable rules for data-driven credit risk assessment using a hybrid method. Adaptive fuzzy network partitioning, rough set theory, and rule generation can improve decision-making across domains

Exploring the synergistic effects of hybrid grid partitioning and rough set method for fuzzy rule generation in dataset classification Abubakullo, Abubakullo; Alesha, Aisyah
International Journal of Enterprise Modelling Vol. 17 No. 2 (2023): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research explores the synergistic effects of hybrid grid partitioning and the rough set method for fuzzy rule generation in dataset classification. The aim is to improve the accuracy and interpretability of the classification process. The rough set-based feature selection technique is employed to identify the most relevant features for classification, leading to a focused and informative feature subset. The hybrid grid partitioning approach combines clustering algorithms and grid-based methods to create an efficient grid structure, capturing the intrinsic data distribution. This enhances the representation and separation of data regions, improving classification accuracy. The generated fuzzy rule base provides interpretable decision rules, enabling domain experts to gain insights into the classification process. The proposed approach strikes a balance between accuracy and interpretability, making it valuable for various domains. However, limitations such as generalizability and scalability should be considered. Comparative analysis with existing methods and real-world case studies would further validate the effectiveness of the approach. Overall, this research contributes to the advancement of dataset classification and provides a novel integrated approach for accurate and interpretable classification.

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

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

Quantum distributed data processing for enhanced big data analysis Alesha, Aisyah; Jr , Cappel Bibri; Dhote , Horvath
Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 4 (2023): Oct: Computing Quantum and Related Fields
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v1i4.28

This research explores the paradigm of Quantum Distributed Data Processing (QDDP) and its transformative potential in the realm of big data applications. Focusing on a Quantum Search Algorithm applied to a distributed dataset, the study illuminates key principles of quantum computing, including superposition and parallelism. Through a numerical example, the efficiency gains and scalability of the algorithm are demonstrated, showcasing its ability to revolutionize distributed data processing. The research underscores the importance of addressing challenges such as quantum error correction and hardware limitations for practical implementation. The findings highlight the considerable advantages of QDDP in handling large-scale distributed data and open avenues for future research, including the optimization of quantum algorithms for diverse applications and the exploration of hybrid quantum-classical approaches. This research contributes to the evolving landscape of quantum computing, providing valuable insights into the potential of Quantum Distributed Data Processing to redefine the efficiency and scope of big data analysis in various domains.

Enhancing Electoral Decision-Making: A Social Learning Network Election Decision Support System Utilizing AHP and PROMETHEE Methods Alesha, Aisyah; Simbolon , Romasinta; Batubara, Juliana; Panjaitan, Firta Sari
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 1 (2024): Jan: CNN and Artificial
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v2i1.36

This In today's digital age, the intersection of technology, democracy, and citizen participation has become increasingly prominent. This research explores the development and application of a Social Learning Network Election Decision Support System (SLNEDSS) using Analytic Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) methods to enhance electoral decision-making processes. By leveraging social learning networks as platforms for information dissemination and deliberative discourse, SLNEDSS empowers citizens to make informed choices that reflect their values, aspirations, and preferences. The integration of AHP and PROMETHEE methods within SLNEDSS provides users with structured frameworks for evaluating electoral alternatives, synthesizing stakeholder preferences, and facilitating transparent and systematic decision-making processes. Through empirical studies, the effectiveness of SLNEDSS in enhancing the quality and inclusivity of electoral outcomes is demonstrated, highlighting its transformative potential in shaping the future of democratic governance. The research also identifies challenges and limitations associated with SLNEDSS, such as algorithmic biases and user adoption, and suggests directions for future research to address these shortcomings. Ultimately, this research contributes to advancing the frontiers of knowledge and innovation in the field of electoral decision support systems, paving the way for a more informed, inclusive, and responsive democracy in the digital age.

Co-Authors Abubakullo, Abubakullo Dhote , Horvath Firta Sari Panjaitan Fristi Riandari Hengki Tamando Sihotang Joan De Mathew Jonhariono Sihotang Jr , Cappel Bibri Juliana Batubara Juliana Batubara Lee , Ricardo Panjaitan, Firta Sari Patricius Michaud Felix Simbolon , Romasinta Sonya Enjelina Gorat

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