Ounongo Repository
The Ounongo Repository (OR) is the institutional repository of Namibia University of Science and Technology. Ounongo means "knowledge. in the Oshiwambo and Otjiherero languages. The OR is administered by the Library, with technical assistance from DICT, and its aim is to collect, organize, manage, store, preserve, publish and make accessible worldwide, the knowledge assets or intellectual output of the University's researchers, staff and post-graduate students. Users may set up RSS feeds to be alerted to new content.
Communities in Ounongo
Select a community to browse its collections.
Recent Submissions
Predictive Modelling of Taxpayer Compliance Behaviour Using Machine Learning At NamRA
(Namibia University of Science and Technology, 2025-07-31) Sifani, Edwin Sifani
This study explored the application of machine learning techniques for predictive modelling of taxpayer compliance behaviour at the Namibia Revenue Agency (NamRA). Multiple classification algorithms were systematically optimised using 5-fold cross-validated Grid Search and Randomised Search, as implemented in the scikit-learn library (v1.2), to enhance predictive accuracy. The hyperparameter search spaces were tailored to each model’s architecture; for instance, Random Forest optimisation included the number of estimators and maximum depth, while Gradient Boosting models emphasised learning rate and structural parameters. The optimisation process yielded notable improvements, with cross-validated accuracy scores ranging from 64% to 68%. The best-performing model, an optimised Random Forest classifier, achieved an accuracy of 68%. These findings demonstrated the efficacy of hyperparameter tuning in improving model performance and underscore the potential of machine learning to support data-driven compliance management at NamRA.
The use of SHAP and LIME in this study provided valuable interpretability of taxpayer compliance predictions, highlighting key factors such as income group, the COVID-19 period, taxpayer registration office, and marital status. These insights align with existing research and reveal how financial capacity, macroeconomic disruptions, and administrative or demographic variables influence compliance. SHAP offers a global view of feature importance, while LIME provides personalised explanations, enhancing trust and communication. Despite modest predictive accuracy, the interpretability benefits support targeted policy interventions and suggest future improvements through richer data and fairness assessments.
Developing A Data-Driven Financial Model for Decision Support in Evaluating Investment Portfolio Performance
(Namibia University of Science and Technology, 2025-09-12) Nghilundwa, Pendapala
Contemporary financial markets demonstrate heightened complexity and volatility, necessitating sophisticated instruments for the precise assessment of investment portfolios. This research explores the application of machine learning (ML) models to predict Month-to-Date (MTD) returns, aiming to enhance financial decision-making. Conventional models frequently exhibit insufficient flexibility to fluctuating market conditions, highlighting the necessity for data-driven approaches that prioritise portfolio-specific metrics, such as Market Value Dirty and Year-to-Date (YTD) returns. Meanwhile, macroeconomic variables such as gross domestic product (GDP) growth, inflation, and interest rates played a secondary role. The study employed a quantitative method, using secondary data from January 2017 to August 2024, which comprised financial measures and macroeconomic variables. Four machine learning models were developed, namely Random Forest, Gradient Boosting, XGBoost and Long Short-Term Memory (LSTM). Data preprocessing and feature engineering played a critical role in model development. Feature engineering involved creating cumulative MTD returns, moving averages (5-day and 10-day), and volatility metrics to capture market trends and risk dynamics. These features were derived from daily returns, grouped by year and month, and calculated using rolling operations. Data normalisation was applied to standardise input variables, and missing values resulting from rolling operations were filled to ensure dataset completeness. The dataset was then divided into training and testing datasets using a 1:1 ratio. Model performance was evaluated using mean squared error (MSE) and R-squared (R²) metrics, with cross-validation assuring robustness. Among the models, Gradient Boosting attained the lowest mean squared error (MSE: 2.39 × 10⁻⁶) and the highest R² (0.922), outperforming Random Forest (MSE: 2.72×10⁻⁶, R²: 0.911), XGBoost (MSE: 3.13×10⁻⁶, R²: 0.898), and LSTM (MSE: 3.98×10⁻⁶, R²: 0.870). Feature importance analysis highlighted Market Value Dirty, YTD Return, and Benchmark (BM) Size as the most influential predictors. At the same time, macroeconomic variables such as interest rates and inflation contributed minimally to short-term forecasting. This demonstrates the dominance of portfolio-specific metrics in predicting MTD returns. While LSTM excelled in capturing temporal relationships, its predictive accuracy lagged due to volatility during high-risk periods. The results affirm the effectiveness of machine learning models in enhancing financial decision-making. Gradient Boosting and Random Forest models offer accurate predictions and valuable insights into key portfolio-specific factors, underscoring their utility for risk management and strategic planning. The dissertation recommends further exploration of hybrid models, the inclusion of additional macroeconomic variables, and the integration of real-time data to enhance predictive accuracy and robustness. These improvements establish data-driven approaches as essential instruments for financial firms operating in unpredictable markets.
A Centralised Hadoop-Based Framework for Big Data Analytics in Prime Ministerial Offices: A Namibian Case Study
(Namibia University of Science and Technology, 2025-09-30) Haitamba, Pombili I.P.
Big Data is reshaping the way governments operate, influencing how decisions are made and services delivered. However, most governments, particularly in developing countries, face challenges in managing the growing volume of data generated across public institutions. These challenges can be attributed to a lack of expertise in handling complex and diverse datasets generated in high volume which are becoming difficult for traditional databases to manage.
In Namibia, the Office of the Prime Minister (OPM) plays a significant role in coordinating governance and public service administration. The OPM has a mandate to oversee Information and Communication Technology (ICT) in the Public Service and promote e-governance initiatives, which involve the usage and handling of data. However, despite its central role, data from different government ministries remains fragmented, building up silos. This fragmentation limits the government's ability to extract value from the data, hinders coordination across ministries, and ultimately leads to poor service delivery.
This study addressed the challenges of data fragmentation within the OPM. It explored the feasibility of designing a centralised database system using Hadoop to integrate and analyse big data across ministries, with the goal of improved administrative efficiency, enhanced public service delivery, and promoting e-governance.
Synthetic datasets were generated in Mockaroo, to represent datasets from government ministries. A Hadoop-based setup was undertaken to simulate a centralised database framework integrating all government ministries’ data in Hadoop Distributed File System (HDFS) for storage. A MapReduce job was run in Hadoop, using Java code for analyses across ministries, from Ministry of Home Affairs, Immigration, Safety and Security (MHAISS), Ministry of Labour (MoL), and Ministry of Education, Arts and Culture (MEAC). The job compared poverty indicators based on birth year, educational achievement, and employment status across the three ministries and aggregated the results for regional poverty analysis.
The findings of this research show that Hadoop is a cost-effective, open-source framework that has the capabilities to store versatile datasets that currently exists within the Namibian public service into one centralised database that supports big data analysis. The processing layer of Hadoop, MapReduce was able to process a job in minutes that would normally take five to seven working days to complete in the OPMs current administration. Adopting this framework would enable the OPM to make informed decisions backed by evidence, eliminate inefficiencies in public service delivery, and enhance public trust through improved service delivery.
Design of a Model for Augmenting Digital Forensics into Information System Audit in The Financial Sector
(Namibia University of Science and Technology, 2024-09) Iipumbu, Ericky
Audits of information systems demonstrate whether IT controls are effective in protecting company property, ensuring data integrity, and aligning with the organisation's overarching objectives. Information System auditors evaluate all components of financial and business controls, including information technology systems and physical and logical security protocols. As long as cyberattacks continue to occur in the financial sector, it is increasingly important to take robust measures to ensure the integrity and security of information systems. New models that incorporate digital forensics methods into the audit process have emerged as a result of the fact that traditional audit processes frequently fail to meet evolving objectives. Based on the findings of a systematic literature review that demonstrated that the evidence-gathering techniques employed in information system audits are inadequate for assessing the efficacy of internal controls, this study proposes a model that integrates digital forensics into information system auditing that was designed using Design Science Research techniques. The model is envisaged to improve the information system auditing process by augmenting digital forensics processes into information system auditing. The model incorporates key digital forensic components embedded into audit procedures to enhance the accuracy and reliability of evidence collection, ensure the integrity and authenticity of digital evidence, and facilitate a more detailed analysis of audit data. The inclusion of these digital forensic techniques is essential for addressing complex cyber threats and fraud within the financial sector, providing auditors with robust tools to conduct more thorough and defensible investigations, and ensuring compliance with industry standards and regulatory requirements. In summary, the proposed approach offers a structured approach to the integration of digital forensic methodologies into the auditing process and establishes guidelines, in a manner that ensures audit opinions for information systems auditors are unqualified rather than qualified and disclaimer.
Examining The Influence of Facebook and Whatsapp as Sources of News Among Youth in Katima Mulilo, Namibia
(Namibia University of Science and Technology, 2024-07-09) MUSHAUKWA, ARON
The influence and impact of social media as sources of news can no longer be ignored. None should turn a blind eye to how it has captured the imagination of young people, who are the majority users of social media. It is crucial to understand that social media has transformed the landscape of information dissemination. Offering unprecedented access, speed, and interaction on a global scale. It has also democratised access to knowledge, empowering individuals to seek and share information instantly across borders and culture. This study explored the influence of Facebook & WhatsApp as sources of news among youth in Katima Mulilo in the Zambezi Region of Namibia. The study found out that social media evokes the feelings of young people and opens their mind to wider understanding of things. Facebook in particular, allows them to participate in deliberations and discussions, and to share their opinions on various news issues. The data collected and analysed indicate that young people in Katima Mulilo see Facebook and WhatsApp as vital sources of news, connecting, networking and communication, and breaks barriers as it is faster and provides instant news regardless of where one is. The study recommends that media organisations should actively engage with young people on their social media pages, by proactively proving access to news at all times. The study further recommends to local news outlets to open WhatsApp channels in order provide unhindered link to their Facebook pages and websites.