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All Journal Journal of Computer Science and Research International Journal of Enterprise Modelling
Alesha, Aisyah
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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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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (489.741 KB) | DOI: 10.35335/emod.v17i2.18

Abstract

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

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
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

Abstract

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

Abstract

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 Jr , Cappel Bibri Juliana Batubara Lee , Ricardo Panjaitan, Firta Sari Simbolon , Romasinta