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All Journal Indonesian Journal of Information System JOURNAL OF APPLIED INFORMATICS AND COMPUTING IJID (International Journal on Informatics for Development) Journal of Information Systems and Informatics Indonesian Journal of Innovation and Applied Sciences (IJIAS) The Indonesian Journal of Computer Science
Ndlovu, Belinda
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences

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Journal : IJID (International Journal on Informatics for Development)

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Twitter (X) Sentiment Analysis on Monkeypox: A Systematic Literature Review Chamboko, Hazel; Maguraushe, Kudakwashe; Ndlovu, Belinda
IJID (International Journal on Informatics for Development) Vol. 14 No. 2 (2025): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2025.5196

Abstract

Monkeypox has a risk of growing into a global threat. Understanding public sentiments is crucial for effective emergency responses, as it helps counter misinformation, enhance communication, and improve the retention and application of public health information. This systematic review of literature aims to provide foundations for identifying existing algorithms, commonly used data collection methods, and pre-processing techniques applied to Twitter discussions on Mpox. The review followed the PRISMA guidelines. Relevant literature was retrieved from ScienceDirect, IEEE, PubMed, and Springer databases, resulting in 15 studies that met the inclusion criteria. Most preprocessing methods include stop word removal, lemmatisation, and tokenisation; commonly used data collection methods include Twitter API, Academic API V2, Snscrape, Twint, and Tweepy. Classification of sentiment tended to be hybrid models like CNN-LSTM or transformer-based models such as BERT, which also perform well in dealing with complex linguistic patterns. These recent models, additionally, addressed other very important issues like misinformation detection, irony, and bot-generated content, which earlier models would often fail to tackle. Despite these advancements, much work still needs to be done in improving the accuracy, generalizability, and interpretability of sentiment analysis models in live monitoring of public health.
Early Detection of Diabetic Retinopathy Through Explainable AI Models: A Systematic Review Ngwazi, Tinashe; Ndlovu, Belinda; Maguraushe, Kudakwashe
IJID (International Journal on Informatics for Development) Vol. 14 No. 2 (2025): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2025.5200

Abstract

Diabetes, if not detected early, can lead to serious complications such as vision loss, known as diabetic retinopathy. Explainable Artificial Intelligence (XAI) can enhance traditional Machine Learning methods, which are not understandable and transparent in diagnostic tasks. This Systematic Literature Review explores data inputs that influence the performance of XAI models in detecting diabetic retinopathy, how XAI techniques can enhance early detection outcomes in diabetic retinopathy, the challenges in implementing these techniques and the ethical implications of using these models in clinical practice. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach guided the search in 4 databases, Springer, Science Direct, PubMed and IEEE Xplore. The findings reveal that XAI techniques like Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (GRAD-CAM) offer opportunities like early detection outcomes, integration with existing clinical processes, enhancing trust in AI systems, improving accuracy and personalised treatment. XAI can also facilitate collaboration among clinicians, maintaining fairness in AI systems and supporting adherence to ethical standards. However, research on clinical validation of these models, as well as standardised performance evaluation metrics, is lacking.
Co-Authors Chamboko, Hazel Chikumo, Onayi Theresa Chipfumbu, Colletor T. Chizengwe, Kudzaishe Lawal Dube, Sibusisiwe Dube, Yangekile Hadebe, Sanele Kiwa, Fungai Jacqueline Mabikwa, Otis Machemedze, Joshua Maguraushe, Kudakwashe Maitireni, Peter Marambi, Sheltar Muderere, Vimbai Alice Muduva, Martin Mugambiwa , Tarisai Muzenda, Hillary Ncube, Vusumuzi Ndlovu, Sharmaine Ngwazi, Tinashe Ngwenya, Taboka Nyamupaguma, Tanyaradzwa Razo, Charnelle Sibanda, Shumirai Sibanda, Thando Vareta, Passmore