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All Journal Mimbar Ilmu Indonesian Journal of Data and Science Journal of Systems Engineering and Information Technology
Oyelakin, Akinyemi Moruff
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Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics

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A Survey of Approaches for Designing Course Timetable Scheduling Systems in Tertiary Institutions Musa, Usman Bala; Oyelakin, Akinyemi Moruff
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 3 No 1 (2024): March 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v3i1.5609

Abstract

Scheduling the course schedule in tertiary institutions is a complex and crucial task. Past studies have pointed out that when scheduling is performed effectively, it influences students' learning experiences, faculty workloads, and overall institutional efficiency. It has also been argued that in the allocation of courses, classrooms, and faculty members, various constraints, preferences, assumptions, dependencies, and objectives must be taken into consideration. This article reviewed different approaches that have been employed in designing course schedule scheduling systems with particular reference to tertiary institutions. Relevant articles were sourced from notable research repositories using identified keywords. The articles obtained were categorized according to the different methods that were used to solve the scheduling problems of course schedules in higher institutions. The review evaluated how each approach addresses the challenges in course time table scheduling. Thereafter, the paper discussed the advantages, limitations, and suitability of these scheduling techniques time-tabling. Additionally, real-world implementations in various tertiary institutions are mentioned. By discussing the strengths and weaknesses of different methodologies in this work, this survey is believed to be a valuable resource for future studies in the area of course scheduling in tertiary institutions.
A Learning Approach for The Identification of Network Intrusions Based on Ensemble XGBoost Classifier OYELAKIN, Akinyemi Moruff
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.88

Abstract

The limitation of signature-based Intrusion Detection systems has given rise for the popularity of Machine learning (ML) approaches r for building such intrusion detection systems (IDSs). ML is a sub-filed of Artificial Intelligence that enables algorithms to learn from data and its applications have been widely accepted and used in many domains. To achieve a promising ML-based model that can identify attacks and intrusions in networks and the cyber space, different stages of machine learning approach like pre-processing, attribute selection, model building, hyper parameter tuning can be very important. CICIDS2017 intrusion dataset was used for all the experimentations. This study focuses on building cyber threat detection model based on the ensemble feature selection and classification method. Innovative approaches were used for the analysis and pre-processing of the dataset. Thereafter, XGboost algorithm was used for selecting relevant features from the default input attributes in each of the captures. Thereafter, the reduced features were employed in the identification of cyber intrusions. The average accuracy achieved in the 8 captures of the dataset is 98% while precision is 0.98. Also, recall is 0.98, f1-score is 0.98 while AUC ROC score is 0.99. The study concluded that XGBoost-based model was able to achieve promising results based on the proper dataset encoding, feature importance-based feature selection and tuning of the algorithm for intrusion identification.
A Survey on Machine Learning Techniques for The Prediction of Solar Power Production Lamidi, L.O.; Oyelakin, Akinyemi Moruff; Akinbi, M. B
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.130

Abstract

Renewable energy sources are needed globally to support the available non-renewable energy sources our day-to-day living. There is high demand for renewable energy sources in both the developed and developing economies. Solar power is a good example of renewable energy source and people are currently embracing it globally for both domestic and industrial uses. Generally, these energy sources are meant to support the hydro, thermal and other energy sources that are available in different countries of the world. With the popularity of solar energy for both domestic and industrial usage, it can be argued that the estimation of the production level of such energy source is necessary so as to achieve proper planning and management. Due to the fact that the availability of the solar energy power depends largely on a number of environmental and weather conditions, predicting its production or generation can be very important. This study surveyed different works in the area of using machine learning techniques for solar power production prediction. The articles sourced were from notable research repositories. This study focuses on articles that were published between 2013 and 2023 on the subject matter. Different types of machine learning (ML) algorithms that have been used to build models from solar energy datasets are reported in this study. It is believed that the work can provide better insights for the researchers working in the problem area. Thus, the insights in this study can lead to building of improved machine learning-based models for solar power forecasting
Unleashing Potential: How Emerging Leaders are Revolutionizing Educational Systems Adeoye, Moses Adeleke; Akinnubi, Paul Olaolu; Makinde, Semiu Olawale; Oyelakin, Akinyemi Moruff; Suleiman, Yusuf
Mimbar Ilmu Vol. 29 No. 2 (2024): August
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/mi.v29i2.71181

Abstract

Today’s world of occupation demands innovative approaches and strategies used by these leaders to revolutionize the existing education system. Therefore, this study aims to investigate the critical role of mentorship, collaboration, and professional development in nurturing and empowering emerging leaders to drive positive change in the education system. By examining the key roles and initiatives undertaken by emerging leaders, this study highlights their significant contributions to education improvement and reform on a global scale. This study uses a comprehensive and multifaceted research methodology to capture the diverse experiences and perspectives of emerging leaders in the education sector. The results of this study seek to underscore the importance of fostering a supportive ecosystem that nurtures and strengthens the impact of emerging leaders in education. This study advocates for a paradigm shift in the leadership paradigm in educational institutions, emphasizing the development of visionary, inclusive, and adaptive leaders who are able to steer the sector towards a more equitable and effective future. In conclusion, this study serves as an interesting resource to understand the dynamic landscape of educational leadership and the strategies used by emerging leaders to revolutionize the traditional paradigm.
Co-Authors Adeoye, Moses Adeleke Akinbi, M. B Akinnubi, Paul Olaolu Lamidi, L.O. Makinde, Semiu Olawale Musa, Usman Bala Suleiman, Yusuf