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This Author published in this journals
All Journal Journal of Information Systems and Informatics Indonesian Journal of Data and Science
Bukohwo Michael Esiefarienrhe
North West University
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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A Hybrid Framework for The Implementation of Business Intelligence Systems in Small Scale Enterprises Teressa Tjwakinna Chikohora; Bukohwo Michael Esiefarienrhe
Journal of Information System and Informatics Vol 4 No 1 (2022): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v4i1.221

Abstract

Small scale enterprises can improve their operations by implementing business intelligence systems. The business intelligence systems are complex and require expertise to ensure successful implementation, hence the need for small scale enterprises to determine their readiness before undertaking the project. To improve chances for successful implementation, this study proposed a framework to guide small scale enterprises on the requirements for business intelligence systems. The design steps defined by Edwards and Goodrich & Tamassia were followed to design the framework. The framework components were informed by the Diffusion of Innovation and Technology Organization and Environment theories, the Information Evaluation Model, and the critical success factors for BIS implementation. A small business may evaluate its resources against the framework components to determine whether to implement a business intelligence system. In future, the framework may be extended to include weights and other criteria to calculate a business’s status.
Medium Range Meteorological Drought Prediction Based on SPEI-3 Using Ensemble Machine Learning and Deep Learning in North West Province, South Africa Reatlegile Phiri; Bukohwo Michael Esiefarienrhe; Ibidun Christiana Obagbuwa
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.354

Abstract

Meteorological drought monitoring is a pivotal action in everyday humankinds’ activities around the globe. It evaluates atmospheric conditions using weather observation instruments to measure atmospheric variables. Due to the highly sophisticated atmospheric environment, errors in drought monitoring and uncertain observation have been observed. Therefore, this research paper develops a lightweight Machine Learning (ML) and Deep Learning (DL) framework to forecast medium term meteorological drought in North West, South Africa using Standardized Precipitation Evapotranspiration Index at 3 -months (SPEI-3) timescale. This time scale reflects moisture deficits directly impacting agricultural production, early warning decisions and water management. The dataset used in this research study was obtained from South African Weather Services through a formal data request submission and not publicly accessible over a period of 10 years. Furthermore, the dataset consists of 20085 data entries and 11 data columns collected from 10 weather stations. The proposed models include SVR-RF, and, CNN-LSTM-ANN, compared to benchmark models, such as SVR, RF, CNN, LSTM, ANN, CNN-LSTM evaluated using statistical metrics, such as MSE, MAE, and . The results demonstrated irregular drought patterns during the defined period with SPEI-3 values clustered below normal conditions. Similarly, validation results showed that SVR demonstrated strong predictive performance with competitive MSE of 0.28, low MAE of 0.34 and  of 0.86. Although, the proposed CNN-LSTM-ANN and SVR-RF models did not exhibit competitive performance compared to benchmarking models, the result provides valuable comprehension, data collection, distribution, architecture, and computational power
Co-Authors Ibidun Christiana Obagbuwa Reatlegile Phiri Teressa Tjwakinna Chikohora