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All Journal Journal International of Lingua and Technology Journal Markcount Finance Lingeduca: Journal of Language and Education Studies International Journal of Educational Narratives Journal of Social Science Utilizing Technology Journal of Social Science Utilizing Technology Journal of Computer Science Advancements Journal of Noesantara Islamic Studies International Journal of Educatio Elementaria and Psychologia Research Psychologie, Orientation et Conseil Journal of Biomedical and Techno Nanomaterials Journal of Social Entrepreneurship and Creative Technology Journal of Humanities Research Sustainability Journal of Multidisciplinary Sustainability Asean
Rahman, Rashid
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Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Journal : journal of computer science advancements

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INTERPRETATION OF DEEP LEARNING MODELS IN NATURAL LANGUAGE PROCESSING FOR MISINFORMATION DETECTION WITH THE EXPLAINABLE AI (XAI) APPROACH muhammadiah, mas'ud; Rahman, Rashid; Wei, Sun
Journal of Computer Science Advancements Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i2.2104

Abstract

The increasing spread of misinformation through digital platforms has raised significant concerns about its societal impact, particularly in political, health, and social domains. Deep learning models in Natural Language Processing (NLP) have shown high performance in detecting misinformation, but their lack of interpretability remains a major challenge for trust, transparency, and accountability. As black-box models, they often fail to provide insights into how predictions are made, limiting their acceptance in sensitive real-world applications. This study investigates the integration of Explainable Artificial Intelligence (XAI) techniques to enhance the interpretability of deep learning models used in misinformation detection. The primary objective of this research is to evaluate how different XAI methods can be applied to explain and interpret the decisions of NLP-based misinformation classifiers. A comparative analysis was conducted using state-of-the-art deep learning models such as BERT and LSTM on benchmark datasets, including FakeNewsNet and LIAR. XAI methods including SHAP (SHapley Additive Explanations), LIME (Local Interpretable Model-agnostic Explanations), and attention visualization were applied to analyze model behavior and feature importance. The findings reveal that while deep learning models achieve high accuracy in misinformation detection, XAI methods significantly improve transparency by highlighting influential words and phrases contributing to model decisions. SHAP and LIME proved particularly effective in providing human-understandable explanations, aiding both developers and end-users. In conclusion, incorporating XAI into NLP-based misinformation detection frameworks enhances model interpretability without sacrificing performance, paving the way for more responsible and trustworthy AI deployment in combating online misinformation.
BIG DATA ANALYTICS FOR SUSTAINABLE GREEN SUPPLY CHAIN MANAGEMENT OPTIMIZATION MODELS Nizam, Zain; Rahman, Rashid; Hakim, Muhammad Arif Abdul
Journal of Computer Science Advancements Vol. 4 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i2.3789

Abstract

The growing need for sustainable practices in global supply chains has driven the adoption of Big Data Analytics (BDA) to optimize performance and reduce environmental impact. Traditional supply chain management systems often fail to balance operational efficiency with sustainability goals, leading to increased waste and resource inefficiency. Big Data Analytics, by providing real-time insights, predictive models, and data-driven decision-making, offers a solution to this challenge. This research explores the application of BDA in the optimization of Sustainable Green Supply Chain Management (GSCM) models, focusing on how data-driven strategies can enhance both environmental and operational performance. The study employs a mixed-methods approach, combining case studies, performance metrics, and interviews with key industry stakeholders to assess the impact of BDA on supply chain efficiency, resource utilization, and waste reduction. The results show that BDA significantly improves key performance indicators, including a 20% increase in resource efficiency, a 25% reduction in waste, and a 15% decrease in operational costs. The study concludes that BDA is a crucial enabler for sustainable supply chains, providing organizations with the tools to optimize operations while minimizing their environmental footprint.
Co-Authors Amien, Muhammad Amir, Syafiq Anis, Nina Anna Dara Arif Muzayin Shofwan, Arif Muzayin Arifudin, Opan Asep Kusmawan Belangi, Siska Putri Buka, Sisilia Prima Yanuaria Chai, Nong CNAWP, Rizal Perlambang Daheri, Mirzon Daroini, Muchlis Eddy Silamat, Eddy Fallah, Saiful Farah, Rina Fariq, Aiman Harris, Robert Hernawati Hernawati Idris, Ronsi Ikhlas, Rifki Zaitul Joshi, Nikhil Judijanto, Loso Mas'ud Muhammadiah Mudinillah, Adam Muh Syafei Muhammad Arif Abdul Hakim Murah Murah Nita Rosmala Dewi Nizam, Zain Nurul Huda Pingki, Anandiya Raden Mohamad Herdian Bhakti Rahman, Shahinur Ratna Susanti Razak, Faisal Sun Wei WIJAYA WIJAYA, WIJAYA Yoga Anjas Pratama Zaki, Amin