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This Author published in this journals
All Journal IJIE (Indonesian Journal of Informatics Education) Indonesian Journal of Information System JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Systems and Informatics
Zvinodashe Revesai
Reformed Church University
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Cost-Optimised IoT Architecture for Real-Time E-Waste Monitoring with Operational Validation Belinda Ndlovu; Zvinodashe Revesai; Kudakwashe Maguraushe
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.v8i2.1553

Abstract

Electronic waste (e-waste) is the fastest-growing solid waste stream worldwide, yet formal collection systems remain limited. Many existing Internet of Things (IoT) solutions emphasize advanced functionality at the expense of cost efficiency and practical deployability. This paper presents a cost-optimized IoT architecture for real-time monitoring of e-waste bins. The proposed system adopts a four-layer architecture integrating ESP32 microcontrollers, ultrasonic sensors for fill-level detection, and infrared sensors for monitoring, supported by a Node.js backend that provides real-time data updates. System validation was conducted through sensor calibration (n = 30), functional testing, stress testing, and cost-performance benchmarking against RFID-, GSM-, and LoRa-based alternatives. Experimental results demonstrate a fill-level accuracy of ±3.2%, temperature precision of ±1.8°C, system reliability of 97.3%, uptime of 98.7%, and an average latency of 2.1 s. The deployment cost was USD 78 per bin, which is approximately 40% lower than comparable RFID-based systems. In addition, the system reduced unnecessary collection trips by 35% and yielded an estimated return on investment (ROI) of 8.5 months. These results show that a low-complexity, cost-efficient IoT design can provide a scalable and practical solution for e-waste bin monitoring.
Exploring Gender-Based Preferences in TikTok Influencer Following Among Students at University of Technology Khulula Blessing Mthembu; Xolisa Philip Poyise; Phomolo Motsilili; Murimo Bethel Mutanga; Zvinodashe Revesai
Indonesian Journal of Information Systems Vol. 8 No. 2 (2026): February 2026
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v8i2.12888

Abstract

The rise of short-form video platforms has fundamentally changed patterns of digital communication, with TikTok becoming a dominant influence in youth media consumption. However, gender-specific preferences in influencer choice remain underexamined, especially within African university settings. Based on Uses and Gratifications Theory (UGT), this qualitative exploratory study investigates how gender affects TikTok influencer following behaviours among 103 South African university students who were active TikTok users. Data were gathered via open-ended online questionnaires and analysed through Qualitative Content Analysis with dual-researcher coding to ensure reliability. Significant gender-based differences appeared in influencer preferences and motivational factors: male students (61% of the sample) mainly followed educational, motivational, and entrepreneurial influencers, seeking cognitive gratifications related to skill development and career prospects, while female students (39% of the sample) inclined towards lifestyle, beauty, and fashion influencers, prioritising affective gratifications like emotional resonance, identity exploration, and self-expression. Despite these differences, both groups valued influencer authenticity, relatability, and expertise over follower count or popularity metrics. The findings extend UGT application to algorithm-driven, short-form video platforms and show how gender influences digital media gratification-seeking behaviours, revealing TikTok as a multifaceted tool for identity building, emotional validation, and aspirational learning rather than just entertainment. These results inform gender-sensitive digital marketing, educational technology integration, and culturally relevant content creation for African youth markets, allowing universities to use these insights for more effective student engagement on social media. The study's focus on a single institution and its cross-sectional design limit broad applicability, indicating that future research should explore long-term influencer relationships and cross-cultural comparisons across African educational contexts. Keywords: Gender differences, social media, influencer preferences, TikTok, university students, Uses and Gratifications Theory
Digital Empowerment in Social Work: Leveraging AI to Enhance Educational Access in Developing Nations Zvinodashe Revesai; Benjamin Tungwa; Telson Anesu Chisosa; Vanessa Runyararo Meki
IJIE (Indonesian Journal of Informatics Education) Vol 8, No 2 (2024): (IJIE) Indonesian Journal of Informatics Education - December
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v8i2.92951

Abstract

Social work education in developing countries faces significant challenges, including limited resources, restricted access to current knowledge, and inadequate training opportunities. This study aims to examine the potential of emerging Artificial Intelligence (AI) technologies in empowering social work students by enhancing access to information through machine translation and intelligent search tools, improving resource availability via virtual simulations and adaptive learning platforms, and integrating AI-powered self-help tools into the curriculum. A qualitative research design was employed, utilizing in-depth interviews with 16 educators and 8 field training officers, along with focus group discussions involving 24 social work students across selected institutions in Zimbabwe. All interviews were audio-recorded with participant consent, with translators assisting where necessary for local languages. Additional data were collected from documents, public reports, learning platforms, and policy papers to provide context on AI adoption strategies. Data were analyzed using thematic analysis, examining cases and models where AI has expanded access to scholarly materials through automated translation services, enabled localized resources through virtual training simulations, and facilitated the incorporation of culturally aligned self-help tools such as AI chatbots and wellness applications. The findings show that, with careful implementation and consideration of the context, artificial intelligence can reduce inequalities in education and enhance students' abilities through personalized learning paths, virtual environments for practice, and automated feedback systems. However, this research points out the need for addressing the digital divide and ethical issues associated with artificial intelligence, including problems of privacy and algorithmic bias. The study concludes by making a call for further research into models of safe and equitable AI integration in social work education.
Transformer-Based Abstractive Text Summarisation for Real-Time Web Applications: A Browser-Integrated System with REST API Architecture Zvinodashe Revesai; Belinda Ndlovu; Kudakwashe Maguraushe
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12559

Abstract

The exponential growth of digital textual content has intensified the need for efficient, accessible summarisation tools that support information processing across academic, professional, and research domains. While Transformer-based abstractive summarisation models have demonstrated strong performance in benchmark settings, their real-world deployment remains limited due to computational complexity and lack of user accessibility. This study presents a lightweight Transformer-based abstractive text summarisation system, operationalised as a Google Chrome extension and supported by a REST API, enabling seamless integration into everyday user workflows. The proposed system employs an encoder–decoder framework leveraging a pre-trained Transformer-based encoder and a sequence-to-sequence decoder with attention, fine-tuned on the CNN/Daily Mail dataset. Quantitative evaluation on the benchmark dataset achieved ROUGE-1, ROUGE-2, and ROUGE-L scores of 38.21, 16.54, and 35.12, respectively, demonstrating competitive performance relative to established neural baselines. To address the limitations of lexical evaluation metrics, a complementary human evaluation was conducted using a Likert-scale assessment across coherence, informativeness, and fluency, yielding mean scores above 4.0, thereby confirming the qualitative effectiveness of the generated summaries. In addition to model performance, system-level evaluation assessed functional correctness, latency, scalability, and usability within a real-world deployment context. The system demonstrated stable performance under concurrent usage, with an average response time of 4.2 seconds per request and positive user feedback, validating its practical applicability. The findings demonstrate that high-quality abstractive summarisation can be effectively operationalised within a lightweight, browser-integrated architecture, thereby bridging the gap between research-stage neural models and accessible end-user applications. This work contributes to deployment-oriented natural language processing by emphasising usability, modularity, and real-world integration as critical dimensions of system design.
Co-Authors Belinda Ndlovu Benjamin Tungwa Khulula Blessing Mthembu Kudakwashe Maguraushe Murimo Bethel Mutanga Phomolo Motsilili Telson Anesu Chisosa Vanessa Runyararo Meki Xolisa Philip Poyise