https://research.adra.ac.id/index.php/jcsa/issue/feed 2026-07-01T11:14:40+07:00 Journal of Computer Science Advancements journal@adra.ac.id Open Journal Systems <p style="text-align: justify;"><strong>Journal of Computer Science Advancements</strong> is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of science, engineering and information technology. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the <strong>Journal of Computer Science Advancements</strong> follows the open access policy that allows the published articles freely available online without any subscription.</p> https://research.adra.ac.id/index.php/jcsa/article/view/4104 2026-07-01T10:59:22+07:00 Soleman Soleman soleman@borobudur.ac.id Ahmed Al Harthy ahmedai@gmail.com

<p>Rapid digital transformation has generated unprecedented volumes of heterogeneous data, creating significant opportunities for predictive analytics while simultaneously increasing challenges related to data quality, scalability, computational complexity, and decision reliability. Conventional predictive models frequently experience performance degradation when processing high-dimensional and continuously evolving Big Data environments. This study aimed to develop and evaluate an integrated Artificial Intelligence framework that combines advanced Big Data mining techniques with hybrid machine learning models to improve predictive accuracy, computational efficiency, and analytical robustness. Quantitative computational research was conducted using large-scale structured and semi-structured datasets processed through data preprocessing, feature engineering, dimensionality reduction, ensemble learning, deep learning, distributed computing, and hyperparameter optimization. Model performance was assessed using accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve, computational time, memory utilization, and scalability. Experimental results demonstrated that the proposed hybrid framework achieved 98.63% prediction accuracy, an AUC-ROC of 0.995, substantially reduced computational time, lower memory consumption, and superior scalability compared with conventional machine learning and deep learning approaches. Statistical analyses confirmed significant performance improvements across all principal evaluation metrics. Findings indicate that integrating intelligent data mining with Artificial Intelligence enhances predictive capability by optimizing the complete analytical pipeline rather than individual algorithms alone, providing a scalable, efficient, and reliable framework for predictive analytics across diverse Big Data application domains.</p> <p>&nbsp;</p>

2026-06-30T00:00:00+07:00 Copyright (c) 2026 Soleman Soleman, Ahmed Al Harthy