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p-Index From 2021 - 2026
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All Journal Journal of Information Systems and Informatics The Indonesian Journal of Computer Science
Lugayizi, Francis
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : Journal of Information Systems and Informatics

 1 
PyLe: An Interactive Tool for Improving Python Syntax Mastery in Non-Computing Students Mbiada, Alain Kabo; Isong, Bassey; Lugayizi, Francis
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.751

Abstract

The learning and mastering of programming language syntax pose a significant challenge for non-computing students. Most teaching approaches and existing educational tools often fail to address this issue. Therefore, this paper introduces an interactive learning environment called PyLe, specifically designed for introductory programming in Python programming courses. We evaluated the effectiveness of PyLe on first-year students at North-West University in South Africa and the University of Yaoundé 1, Cameroon. Firstly, the study conducts an experiment to assess the effect of PyLe on the time taken to solve a problem and the response quality. Secondly, PyLe’s usability and its instructional value were evaluated by the students and the instructors, respectively. The results from post-test method and a quantitative survey indicate that PyLe improves students’ ability to learn and master program syntax and has a high usability rate. Moreover, feedback from students and teachers affirms PyLe’s potential to address programming syntax challenges for non-computing students. However, the analyses revealed no real relationship between the time taken to complete a task in PyLe and the quality of the solution. This study contributes to improving the teaching and learning of computer programming, which has been considered difficult for both computing and non-computing students.
Machine Learning Algorithms to Defend Against Routing Attacks on the Internet of Things: A Systematic Literature Review Sejaphala, Lanka Chris; Malele, Vusimuzi; Lugayizi, Francis
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.828

Abstract

The Internet of Things (IoT) has become increasingly popular, opening vast application possibilities in different fields including smart cities, healthcare, manufacturing, agriculture, etc. IoT comprises resource-constrained devices deployed in Low Power and Lossy Networks (LLNs). To satisfy the routing requirements of these networks, the Internet Engineering Task Force (IETF) created a standardised Routing Protocol for low-power and Lossy Networks (RPL). However, this routing protocol is vulnerable to routing attacks, prompting researchers to propose several techniques to defend the network against such attacks. Machine learning approaches demonstrate effective ways to detect such attacks in large quantities. Therefore, this paper systematically synthesised 17 publications to compare the performance of traditional and advanced machine learning algorithms to identify the best algorithm for detecting RPL-based IoT routing attacks. The findings of this paper show that machine learning algorithms are capable of effective detection of many routing attacks with high accuracy and a low False Positive Rate. Furthermore, the results demonstrate that on average, advanced machine learning algorithms can achieve an accuracy of 96.03% compared to traditional machine learning algorithms which achieved 91.67%. Traditional machine learning algorithms demonstrated the best performance on average False Positive Rate by achieving 2.75% compared to their counterparts which gained 4.79%. However, Random Forest showed the best performance and outperformed all the algorithms in the selected publications by achieving over 99% accuracy, precision and recall.
Co-Authors Isong, Bassey Malele, Vusi Malele, Vusimuzi Mbiada, Alain Mbiada, Alain Kabo Sejaphala, Lanka Chris