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Journal : big data analytics and data science

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Evaluating Explainable Artificial Intelligence Methods for Interpretable Machine Learning Models in Large Scale Enterprise Data Analytics Systems Indra Ava Dianta; Greget Widhiati; Andreas Tigor Oktaga
Big Data Analytics and Data Science Vol. 1 No. 1 (2026): March: Big Data Analytics and Data Science
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/bdas.v1i1.19

Abstract

Explainable Artificial Intelligence (XAI) has become a critical area of research within artificial intelligence, focusing on improving the transparency and interpretability of machine learning (ML) models, often referred to as "black-box" models. The need for XAI techniques arises from the inherent complexity of ML models, which can make their decision-making processes difficult for users to understand. This study investigates various XAI techniques, including LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), to assess their impact on model interpretability without significantly compromising predictive performance. A comparative experimental design was used, applying these XAI methods to different ML models, including deep neural networks and ensemble methods, within large-scale enterprise data analytics systems. The results indicate that XAI methods significantly enhance model transparency and decision traceability, allowing users to understand the influence of individual features on predictions. While a slight reduction in predictive accuracy was observed, especially with simpler models, the trade-off between interpretability and performance was deemed acceptable, particularly in fields requiring transparency, such as healthcare, finance, and autonomous systems. The use of XAI in enterprise data systems has practical implications for fostering trust and enabling informed decision-making among stakeholders. Furthermore, the study discusses the challenges and limitations of applying XAI techniques, such as complexity, scalability, and model-specific limitations. Future research is suggested to focus on developing more scalable and efficient XAI methods, enhancing their applicability across various model types, and addressing the challenges of real-time applications. This will be crucial in ensuring the widespread adoption of XAI in critical domains, promoting the ethical use of AI while maintaining predictive accuracy.
Co-Authors Ade David Ajiwijaya Adriana, Myra Agus Priyadi Agustinus Budi Santoso Agustinus Budi Santoso Ahmad Ashifuddin Aqham Ahmad Zaenudin Ahmad Zainudin Ahmad Zainudin Amad Maijun Amad Maijun Amalia Nur Risa Purnamasari Andik Prakasa Hadi Andik Prakasa Hadi Andreas Tigor Oktaga, Andreas Andriana, Myra Aqnes Wulandari Arie Atwa Magriyanti Arie Yuniarta Ashifuddin Aqham, Ahmad Ayu Agsya Bagus Sudirman Bagus Sudirman Bayu Anggoro Tirta Kencana budi hartono Budi Hartono Budi Hartono Budi Raharjo Cheesya Siska Adhiati Danang Danang Danang Danang Danang, Danang Dendy Kurniawan Dendy Kurniawan Desy Setyowati Devi Ayu Indriyani Dewi Widyaningsih Dewi Widyaningsih Dwi Setiawan Dwi Setiawan Dwi Setiawan Edwin Zusrony Edy Jogatama Purhita Edy Siswanto Eka Nuryanto Budi Susila Eka Pradana , Yudha Eko Siswanto Eko Siswanto Endang Kustami Fatkhul Amin Febri Adi Prasetya Fransisca Khurnia Erkhani Greget Widhiati Greget Widhiati Haris Ihsanil Huda Haryo Kusumo Hendri Rasminto Ida Hendriyani Ihsanil Huda, Haris Ilham Ramasyahdani Jarot Dian Susatyono Kusumaningtyas, Dhevi Dadi Kusumo, Haryo Laksamana Rajendra Haidar Azani Fajri Magriyanti, Arie Atwa Moh Muthohir Moh Muthohir Moh. Muthohir Muh Sabiq Nuris Dwi Setiawan Nuris Dwi Setiawan Nusril Nusril Priyadi Priyadi Ratnaningrum Reni Veliyanti Salman Al Farizi Selli Selfiyanti Setiawan, Nuris Dwi Sherly Dwi Ditzi Savitri Sinaga, Herty Ramayanti Siti Kholifah Siti Kholifah Sri Arttini Dwi Prasetyawati SRI LESTARI sudirman, bagus Tantik Sumarlin . Tanzila Azizah Rahmi Teguh Setiadi Thelsi Seubelan Thelsi Seubelan Tofani Wulandari Vera Sari Gunawan Wicaksono, Jentoro Widya Ariyani Winarto, Yudha Yuli Fitrianto