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All Journal Journal of Information Systems and Informatics The Indonesian Journal of Computer Science
Chimbo, Bester
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Evaluating the Impact of Deep Learning Model Architecture on Sign Language Recognition Accuracy in Low-Resource Context Moape, Tebatso; Muzambi, Absolom; Chimbo, Bester
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4493

Abstract

Deep learning models are well-known for their reliance on large training datasets to achieve optimal performance for specific tasks. These models have revolutionized the field of machine learning, including achieving high accuracy rates in image classification tasks. As a result, these models have been used for sign language recognition. However, the models often underperform in low-resource contexts. Given the country-specific nature of sign languages, this study examines the effectiveness and performance of Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), hybrid model (CNN + Recurrent Neural Networks (RNN)), and VGG16 deep learning architectures in recognizing South African Sign Language (SASL) under a data-constrained context. The models were trained and evaluated using a dataset of 12420 training images representing 26 static SASL alphabets, and 4050 validation images. The paper's primary objective is to determine the optimal methods and settings for improving sign recognition models in low-resource contexts. The performance of the models was evaluated across multiple image dimensions trained for 60 epochs to analyze each model's adaptability and efficiency under varying computational parameters. The experiments showed that the ANN and CNN models consistently achieved high accuracy with lower computational requirements, making them well-suited for low-resource contexts.
Advancing Inclusive Educational VR: A Bibliometric Study of Interface Design Maguraushe, Kudakwashe; Masimba, Fine; Chimbo, Bester
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1271

Abstract

While virtual reality (VR) has shown transformative potential in education, its accessibility and inclusivity for learners with disabilities remain insufficiently explored. This study offers the first bibliometric mapping of educational VR interface design for inclusivity, analysing 4,735 documents from 1,714 sources (2020-2025) using Biblioshiny and VOSviewer. The analysis reveals a 13.22% annual publication growth rate, an average of 10 citations per document, and an international co-authorship rate of 25.85%, reflecting both rapid expansion and increasing collaboration. Dominant research themes include user experience, usability, and the metaverse, while underexplored areas such as cognitive accessibility and neurodiverse learners highlight emerging opportunities. The findings demonstrate a concentration of scholarly activity in North America and Asia, with limited representation from the Global South. Practically, the study informs developers on designing adaptive interfaces, guides educators in implementing inclusive VR pedagogies, and provides policymakers with evidence for promoting equitable digital learning ecosystems. By identifying trends, gaps, and collaboration patterns, this research advances the discourse on inclusive educational VR and underscores the need for interdisciplinary, AI-driven accessibility strategies that ensure equitable participation for all learners.
Integrating Human-Centered AI into the Technology Acceptance Model: Understanding AI-Chatbot Adoption in Higher Education Masimba, Fine; Maguraushe, Kudakwashe; Chimbo, Bester
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1316

Abstract

Artificial intelligence (AI) is transforming education by enhancing assessments, personalizing learning, and improving administrative efficiency. However, the adoption of AI-powered chatbots in higher education remains limited, primarily due to concerns about trust, transparency, explainability, perceived control, and alignment with human values. While the Technology Acceptance Model (TAM) is commonly used to explain technology adoption, it does not fully address the challenges posed by AI systems, which require human-centered safeguards. To address this gap, this study extends TAM by incorporating Human-Centered AI (HCAI) principles—explainability, transparency, trust, and perceived control—resulting in the HCAI-TAM framework. An empirical study with 300 respondents was conducted using a structured English questionnaire, and regression analysis was applied to assess the relationships among variables. The model explained 65% (R² = 0.65) of the variance in behavioral intention and 55% (R² = 0.55) in usage behavior. The findings highlight that integrating HCAI principles into TAM enhances user adoption of AI chatbots in higher education, contributing both theoretically and practically.
Co-Authors Maguraushe, Kudakwashe Masimba, Fine Moape, Tebatso Muzambi, Absolom