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UML Design of Business Intelligence System for Small-Scale Enterprises Esiefarienrhe, Bukohwo Michael; Moemi, Thusoyaone Joseph
Journal of Information System and Informatics Vol 6 No 1 (2024): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i1.672

Small scale enterprises (SSEs) face numerous challenges in managing and processing their business data, which often leads to inefficiencies, errors, and suboptimal decision-making. To address these challenges, the design of a Business Intelligence (BI) system for SSEs using Unified Modeling Language (UML) diagrams is proposed. UML diagrams provide a visual modeling language that facilitates the design, analysis, and implementation of complex systems. The proposed BI system is designed to enable SSEs to gather, integrate, analyze, and present data from various sources, including sales, finance, operations, and customer relations. The agile methodology was used in the design of the mobile intelligence system (MoIS) utilizing the Scrum method because of its time-boxed iterations (sprints), cross-functional team collaboration, and regular feedback loops. The UML diagrams used in this design are use case diagrams, activity diagrams, class diagrams, and sequence diagrams. The use case diagrams identify the system's users and their interactions with the system, while the activity diagrams describe the system's processes and workflows. The class diagrams depict the system's data structures and relationships, and the sequence diagrams specify the interactions between system components. The proposed BI system provides SSEs with the necessary tools to make informed decisions, improve operational efficiency, and gain a competitive advantage. The UML-based design approach ensures that the BI system is well-structured, easy to maintain, and scalable. The effectiveness of the proposed BI system is demonstrated through a case study of an SSE in the retail industry. The results indicate that the BI system improves the SSE's decision-making processes and enables it to respond more quickly to changing market conditions. The proposed BI system using UML diagrams is a valuable contribution to the field of BI systems design and is expected to benefit SSEs in various industries.

Framework for Intelligent-Electricity Billing and Consumption Information System (IEBCIS) Esiefarienrhe, Bukohwo Michael; Maine, Itumeleng Michael
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.894

The increasing demand for energy, coupled with the depletion of natural resources and environmental concerns, necessitates the adoption of sustainable energy practices. The use of electricity sustainability principles and smart meters data integration into energy systems plays a crucial role aiding electricity users make informed decisions about their energy consumption, driving sustainable energy practices and improving environmental stewardship. Despite efforts at electricity innovations, challenges persist in improving existing electricity frameworks, particularly in enhancing security and privacy measures, optimizing energy efficiency, improving user engagement and awareness, and addressing sustainability, scalability, interoperability, reliability and mounting environmental concerns and global warming issues. The integration of the principles of electricity sustainability and smart meter data into the development of a framework for Intelligent-Electricity Billing and Consumption Information System (IEBCIS) is crucial and an optimized approach to tackle these issues moving forward. Therefore, this paper presents IEBCIS framework that incorporated key aspects of electricity sustainability, interoperability, scalability, usability, reliability, security and privacy in the design of its framework. The Action Design Research (ADR) methodology using the pragmatism research philosophy was used to develop software prototypes to elucidate requirements for testing the framework. The result from the prototype showed significant potential to transform electricity billing and consumption practices by empowering users to make informed decisions about their energy usage, driving sustainable energy practices and improving environmental stewardship. Using the prototype, electricity consumers were able to access their smart data, query their electricity bills, simulate various electricity reduction best practices and view their total energy proposed savings in rands (South Africa currency). Ultimately, the IEBCIS framework achieved its aim of contributing to a more efficient and sustainable energy ecosystem, aligning with the global imperative for sustainable energy practices.

Framework for Project Sustainability for Power Installations Using Business Intelligence Approach: A Systematic Literature Review Esiefarienrhe, Bukohwo Michael; Maine, Itumeleng Michael
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3402

The growing concerns about environmental sustainability and energy conservation have led to increased interest in optimizing electricity consumption and billing processes in various projects. This research article presents a comprehensive study on the development and application of Business Intelligence (BI) frameworks for enhancing project sustainability through data-driven energy management. Through the integration of BI tools and techniques, this research investigates the analysis of electricity consumption patterns, billing accuracy, and cost-effectiveness in diverse project contexts. The article emphasizes the significance of data preprocessing, statistical analysis, and predictive modelling in uncovering valuable insights to support informed decision-making. Additionally, the review examines the concept of project sustaibility, emphasizing its significance in achieving desired outcomes, meeting stakeholder expectations, and ensuring the project’s viability in an ever-changing environment. Traditional project management approaches often fail to adequately address sustainability concerns, leading to project failures or limited long-term impact. Hence, the review highlights the growing importance of leveraging BI-driven frameworks to enhance project sustainability in various sectors, including in Information and Communication Technology (ICT) domain. The Systematic literature review (SLR) method was used involving the scooping of 230 articles from over 8 global academic databases. With the use of exclusion criteria, only 61 articles were used in the study. The analysis of the articles shows that 57% were journal articles, 39% were conference proceedings, 2% were thesis/dissertations and 2% were generic. Within the scope of this literature review, key terms and keywords were identified to provide insights into the development of a novel BI-driven framework for project sustainability. Consequently, future research directions are identified to further explore the integration of renewable energy sources, AI and machine learning applications, and behaviour-based energy management strategies within BI frameworks for sustainable project outcomes. This review lays the foundation for future research endeavours in developing innovative BI-driven frameworks that foster sustainable practices and contribute to a greener and more resilient future across diverse industries and projects.

Risk assessment using Business Intelligence framework for organizations: A Systematic Literature Review Esiefarienrhe, Bukohwo Michael; Khutswane, Thoriso
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3457

Today many organizations are facing different kinds of risks as they are migrating towards new technologies. Risk assessment is one of the methods that were developed to help with identifying, assessing, and managing risks. Several studies have been conducted regarding risk assessment and business intelligence, however few studies have been conducted on how both can be integrated and use. A systematic literature review is conducted to understand the importance of risk assessment and business intelligence for organizations. Due to the relevance of BIS and the need for risk analysis before development and implementation, there is need to critically examine literaure to understand what has been done and the gap that exist for future researchers to implement. Therefore this study use the Systematic Literature Review methodology to collect 125 academic publications from over 6 academic, business and research databases for review. Inclusion and exclusion criteria were applied to the papers proning them to 65 and when quality assessment were applied, the total paper obtained amounted to 25 which were eventually used for the review. The results obtained from the review study showed that although there are much publication related to business intelligence and risk assessment, most of them did not incorporate quality assessment, rigorous testing, creation of new analytical tools, application of AI, and deep learning algorithms into developing their business intelligence systems. Future research focus areas resulting from this study were also highlighted in the conclusion session of this study.

Automatic wildlife species identification on camera trap images using deep learning approaches: a systematic review Mamapule, Siyabonga; Esiefarienrhe, Bukohwo Michael; Obagbuwa, Ibidun Christiana
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp968-977

The foundation of systematic research depends on precise species identification, functioning as a critical component in the processes of biological research. Wildlife biologists are prompting for more effective techniques to fulfill the expanding need for species identification. The rise in open source image data showing animal species, captured by digital cameras and other digital methods of collecting data, has been monumental. This rapid expansion of animal image data, integrated with state-of-the-art machine learning techniques such as deep learning which has shown significant capabilities for automating species identification. This paper focuses on the role of deep neural network architectures in furthering technological advancements in automating species identification in recent years. To advocate further investigation in this field, an examination of machine learning architectures for species identification was presented in this work. This examination focuses primarily on image analyses and discusses their significance in wildlife conservation. Fundamentally, the aim of this article is to offer insights into the present advancements in automating species identification and to act as a reference for scholars who are keen to integrate machine learning techniques into ecological studies. Systems designed through Artificial Intelligence are extensive in providing toolkits for systematic identification of species in the upcoming years.

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