Article Per Year (5 Year)
p-Index From 2021 - 2026
0.751
P-Index
This Author published in this journals
All Journal Journal of Information Systems and Informatics The Indonesian Journal of Computer Science
Chimbo, Bester
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : The Indonesian Journal of Computer Science

 1 
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
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.
Ethical Adoption of AI-Powered EdTech in Higher Education: Human-AI Interaction through an Ethically Extended UTAUT2 Model Masimba, Fine; Maguraushe, Kudakwashe; Chimbo, Bester
The Indonesian Journal of Computer Science Vol. 15 No. 1 (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.v15i1.5079

Abstract

This study addresses the need for responsible AI adoption in higher education by developing a human-centred ethical extension of the UTAUT2 model. It integrates two new constructs; AI fairness and human autonomy support and three ethical moderators: ethical risk awareness, perceived algorithm bias and user autonomy concern. To validate the framework, an empirical investigation was conducted with 400 respondents using a structured questionnaire, with data analyzed via regression. All sixteen hypotheses were supported. The model demonstrated strong predictive power, explaining 72.2% of the variance in behavioural intention and 69.1% in use behaviour. The results provide meaningful insights into how ethical perceptions influence adoption. Ultimately, the framework offers practical guidance for policymakers, educators and developers to ensure fair, trustworthy and human-centric AI integration in learning environments.
Co-Authors Maguraushe, Kudakwashe Masimba, Fine Moape, Tebatso Muzambi, Absolom