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All Journal IT JOURNAL RESEARCH AND DEVELOPMENT Journal of Information Systems and Informatics Indonesian Journal of Electrical Engineering and Computer Science Studies in Learning and Teaching The Indonesian Journal of Computer Science
Isong, Bassey
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Other

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A Comparative Study of Computer Programming Challenges of Computing and Non-Computing First-Year Students Mbiada, Alain; Isong, Bassey; Lugayizi, Francis
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3330

Abstract

The learning of computer programming comes with unique difficulties that vary among students depending on their backgrounds, learning methods, and objectives. This paper investigates the programming challenges first-year students from non-computing at the North-West University, South Africa, and computing backgrounds at the University of Dschang, Cameroon face. A questionnaire-based data collection method is utilized and categorizes participants based on their gender, age, fields of study, prior experiences in mathematics, statistics, English, and programming languages, lab use/access, learning strategies, and material preferences. The aim is to identify and analyze the student's understanding of the basic programming concepts and the specific challenges met during introductory programming modules. Analysis of the collected data shows that while a considerable percentage of non-computing students have prior experience in mathematics and English, they lack familiarity with programming. Equally, while most computing students are proficient in spoken English, they face significant challenges in programming, mathematics, and written English. Notable difficulties are experienced in grasping concepts like recursion, arrays, error handling, and function/procedure methods. Moreover, a comparative study reveals that both groups of students encounter similar challenges, however, non-computing students’ difficulties are more than their computing counterparts. This paper, therefore, suggests designing teaching methods and learning materials to specifically meet the needs of non-computer science students, and enhance their understanding and proficiency in computer programming.
Computational Task Scheduling Across IoT-Edge-Fog-Cloud Continua: Algorithms, Adaptability and Research Gaps Isong, Bassey; Mamidza, Fulufhelo
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.5104

Abstract

Internet of Things (IoT) deployments span heterogeneous infrastructure such as edge devices, fog nodes, and cloud servers, each with distinct computational capacity, energy constraints, and cost profiles. Scheduling across this three-tier stack requires satisfying four competing demands, including latency bounds, energy budgets, workload distribution, and cloud offloading cost. None of these can be optimized in isolation, and workload variability across deployment sites makes the problem even harder. In this paper, we review task scheduling strategies in edge-fog-cloud environments. It compares heuristic, metaheuristic, and machine learning-based approaches across deployment settings, adaptation capacity, and measured performance. Findings reveal metaheuristic methods reduce MK and energy consumption; learning-based approaches improve latency and task success rates, though under narrower conditions. Yet widespread reliance on simulation‑based evaluation and task-independence assumptions limits what these results actually demonstrate. Fixed objective weighting, unvalidated scalability, missing workflow dependency support, and static priority schemes each constrain deployment in practice. Future research should therefore prioritize shared or validated testbeds, workflow-aware/dependencies scheduling formulations, variable objective priorities, and scalability studies beyond small-to-medium topologies. Our study establishes a basis for designing scheduling strategies that hold under real deployment conditions across IoT, fog, and cloud applications and production settings.
Resource-Efficient Hybrid Ensemble ML Framework for Anomaly Detection in IoT Smart Homes Kgote, Otshepeng; Isong, Bassey
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.5122

Abstract

The Internet of Things (IoT) technologies are used to support smart home systems through device and sensor connectivity for data exchange. However, the growth in adoption increases exposure to cyber-attacks and device faults, which puts system reliability and user safety at risk. This study proposes a framework that uses a pre-trained hybrid ensemble model to detect and separate attacks and faults while supporting timely mitigation. Firstly, the study evaluates models on the CICIoT2023 and IntelLab fault-injected datasets using ensemble learning methods and traditional supervised classifiers. Extreme Gradient Boosting shows the strongest intrusion detection performance. Random Forest shows the strongest fault detection performance. Secondly, both models were fine-tuned and combined within a hybrid meta-model. The results show high accuracy, strong F1 scores, and low false positive rates. The framework was implemented as a web application using Flask and Streamlit to support real-time simulations of attack, fault, and normal events. Evaluation reports latency under 5 seconds and memory use under 400 KB, which supports deployment on resource constrained IoT devices. It was optimized using quantisation and compression. The paper proposes a hybrid ensemble approach for joint fault and intrusion detection, a deployable prototype for constrained environments, and methods to enhance model performance.
A Dependency- and Trust-Aware Task Scheduling Framework for Efficient Internet of Things Edge Systems Mamidza, Fulufhelo Hopewell; Isong, Bassey
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1489

Abstract

The rapid growth of the Internet of Things (IoT) has significantly increased the number of connected devices, generating massive volumes of data and placing substantial demands on edge and fog computing infrastructures. Traditional resource management approaches often overlook task dependencies, which can lead to inefficient resource utilization, increased execution delays, reduced reliability, and potential security risks in distributed IoT environments. To address these challenges, this paper proposes an improved dependency-aware task scheduling framework designed to operate between edge devices and edge servers. The framework employs directed acyclic graph (DAG) modeling to represent task dependencies and execution order, trust-aware node selection to avoid malicious, overloaded, or unreliable nodes, and Particle Swarm Optimization (PSO) to support adaptive resource allocation under dynamic and heterogeneous workloads. Experimental results demonstrate that the proposed framework achieves an average latency of 50 ms, throughput of approximately 500 transactions per second (tps), and a task completion rate of 98%. These findings indicate that the proposed approach outperforms conventional scheduling methods by improving latency, throughput, reliability, security, and overall task execution efficiency in IoT-enabled edge computing environments.
Co-Authors Elegbeleye, Femi Igo, Ekoro Kgote, Otshepeng Koketso, Ntshabele Lugayizi, Francis Mamidza, Fulufhelo Mamidza, Fulufhelo Hopewell Marivate, Vukosi Mbiada, Alain Mbiada, Alain Kabo Modupe, Abiodun Moeti, Kedibone Ndouvhada, Sedzani Ntshabele, Koketso Rananga, Seani Sebopelo, Rodney