cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Adam Mudinillah
Contact Email
adammudinillah@staialhikmahpariangan.ac.id
Phone
+6285379388533
Journal Mail Official
adammudinillah@staialhikmahpariangan.ac.id
Editorial Address
Jorong Kubang Kaciak Dusun Kubang Kaciak, Kelurahan Balai Tangah, Kecamatan Lintau Buo Utara, Kabupaten Tanah Datar, Provinsi Sumatera Barat, Kodepos 27293.
Location
Kab. tanah datar,
Sumatera barat
INDONESIA
Journal of Computer Science Advancements
Published by Yayasan Adra Karima Hubbi
ISSN : 30263379     EISSN : 3024899X     DOI : https://doi.org/10.70177/jsca
Core Subject : Science,
Computer Science & IT
Journal of Computer Science Advancements 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 Journal of Computer Science Advancements follows the open access policy that allows the published articles freely available online without any subscription.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1 Documents
 1 
Search results for , issue "vol. 4 no. 3 (2026)" : 1 Documents clear
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.

Page 1 of 1 | Total Record : 1

 1 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol. 4 No. 4 (2026) Vol. 4 No. 3 (2026) Vol. 4 No. 2 (2026) Vol. 4 No. 1 (2026) Vol. 3 No. 6 (2025) Vol. 3 No. 5 (2025) Vol. 3 No. 4 (2025) Vol. 3 No. 3 (2025) Vol. 3 No. 2 (2025) Vol. 3 No. 1 (2025) Vol. 2 No. 6 (2024) Vol. 2 No. 5 (2024) Vol. 2 No. 4 (2024) Vol. 2 No. 3 (2024) Vol. 2 No. 2 (2024) Vol. 2 No. 1 (2024) Vol. 1 No. 6 (2023) Vol. 1 No. 5 (2023) Vol. 1 No. 4 (2023) Vol. 1 No. 3 (2023) Vol. 1 No. 2 (2023) Vol. 1 No. 1 (2023) More Issue