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All Journal SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Sains Teknologi dan Sistem Informasi Jurnal Pengabdian Pada Masyarakat METHABDI TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal Sistem Informasi dan Teknologi Jaringan Journal of Computer Science Advancements
Mirza Ilhami
Universitas Mikroskil
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : journal of computer science advancements

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INTELLIGENT AGENT SYSTEMS FOR ADAPTIVE DECISION MAKING IN LARGE SCALE SMART ENVIRONMENTS Aiman Fariq; Nina Anis; Mirza Ilhami
Journal of Computer Science Advancements Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i1.3396

Abstract

The rapid growth of large-scale smart environments, including smart cities, autonomous transportation systems, and smart grids, has necessitated advanced decision-making mechanisms capable of adapting to dynamic and complex conditions. Intelligent agent systems (IAS) offer a promising solution by enabling decentralized, autonomous decision-making based on real-time data. This study explores the application of IAS for adaptive decision-making in large-scale smart environments, focusing on the challenges of scalability, resource allocation, and system responsiveness. The primary objective is to design and evaluate an intelligent agent system capable of operating efficiently in diverse, complex environments. A mixed-methods approach was used, combining simulations and real-world implementations in various smart environments, including energy grids, smart cities, and industrial automation systems. The results indicate that while IAS can perform effectively in smaller environments, performance decreases in large-scale systems due to increased agent interaction and data complexity. Scalability and adaptability remain significant challenges, with response times and resource allocation efficiency declining as the system size grows. The study concludes that further advancements are required in communication protocols and machine learning algorithms to enhance the scalability and real-time decision-making of IAS in large, interconnected systems.
ARTIFICIAL INTELLIGENCE MODELS FOR PREDICTIVE ANALYTICS USING BIG DATA MINING TECHNIQUES Soleman Soleman; Ahmed Al Harthy; Mirza Ilhami
Journal of Computer Science Advancements Vol. 4 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i3.4104

Abstract

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.
Co-Authors Afen Prana Utama Sembiring Ahmed Al Harthy Aiman Fariq Apriyanto Halim Culita Detti Meilandri Elaine Elaine Elly, Elly Erlanie Sufarnap Fendy Sanjaya Hilda Yuliastuti Irpan Pardosi Irwansyah Kelvin Kelvin Khairani Puspita Kristian Heri Natal Lumban Batu Kristian Telaumbanua Leonardo Tanuwijaya Nina Anis Pangaribuan, Jefri Junifer Richard Halim Riche Riche soleman soleman Sudarto Sunario Megawan Syahril Syanti Irviantina