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IJID (International Journal on Informatics for Development)
Published by Universitas Islam Negeri Sunan Kalijaga Yogyakarta
ISSN : 22527834     EISSN : 25497448     DOI : -
Core Subject : Science,
Computer Science & IT
One important point in the accreditation of higher education study programs is the availability of a journal that holds the results of research of many investigators. Since the year 2012, Informatics Department has English language. Journal called IJID International Journal on Informatics for Development. IJID Issues accommodate a variety of issues, the latest from the world of science and technology. One of the requirements of a quality journal if the journal is said to focus on one area of science and sustainability of IJID. We accept the scientific literature from the readers. And hopefully these journals can be useful for the development of IT in the world. Informatics Department Faculty of Science and Technology State Islamic University Sunan Kalijaga.
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Articles 234 Documents
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A Hybrid Approach of Pearson Correlation and PCA in Feature Selection for Opinion Mining Tri Romadloni, Nova; Kurniawan, Wakhid; Ariyadi, Muhammad Yusuf; Efendi, Burhan
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.2025.5195

Abstract

This study proposes a hybrid feature selection approach that combines Pearson Correlation and Principal Component Analysis (PCA) to improve classification performance in opinion mining tasks. The rapid growth of e-commerce on social media platforms, such as TikTok, has generated a significant volume of user-generated reviews, which are valuable sources of consumer sentiment. However, the high dimensionality of textual data poses challenges in achieving accurate sentiment classification. To address this issue, the proposed method first applies Pearson Correlation to remove irrelevant features with weak correlation to sentiment labels, followed by PCA to reduce dimensionality. The dataset consists of user reviews from the TikTok Seller platform. Experiments using SVM, Naive Bayes, and Random Forest show that the hybrid approach achieves the highest accuracy of 86.2% (SVM and RF), improving over PCA-only by +0.9% and recovering 13.8% accuracy loss for Naive Bayes (from 72.0% to 83.1%). The results demonstrate that integrating correlation- and projection-based methods yields a more compact and effective feature set. This approach is especially suited for opinion mining in noisy, high-dimensional e-commerce data.
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.
LDA Topic Modeling Analysis of Public Discourse on Indonesia’s Free Nutritious Meals Program (MBG) Cici Suhaeni; Mualifah, Laily Nissa Atul; Wijayanto, Hari
IJID (International Journal on Informatics for Development) Vol. 14 No. 1 (2025): IJID June
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.2025.5211

Abstract

This study investigates public discourse on Indonesia's Free Nutritious Meals (Makan Bergizi Gratis/MBG) program through Latent Dirichlet Allocation (LDA) topic modeling of YouTube comments. Filling a research gap on online public opinion regarding the MBG policy, this study identifies dominant themes and discursive patterns in public perception. A three-topic model, validated through coherence score evaluation and pyLDAvis visualization, reveals key topics: concerns over food prices and distribution, perceived benefits for children and society, and emotionally and politically driven reactions. The findings provide valuable insights into public opinion, while also highlighting challenges in processing Indonesian-language text, such as informal language and noisy data. This study contributes to understanding public perceptions of social policies in digital environments and recommends future research directions, including improved text preprocessing and alternative topic modeling approaches. By shedding light on online public discourse, this research informs policymakers and stakeholders about the effectiveness and potential areas for improvement in the MBG program.

Page 24 of 24 | Total Record : 234

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