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All Journal Data Science Insights Big Data Analytics and Data Science Business System & Innovation Journal (BSIJ)
Amelia Contesa
Universitas Lancang Kuning
Computer Science & IT Engineering

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A Framework for Scalable Big Data Analytics and Workflow Orchestration in Heterogeneous Cloud Native Software Platforms for Smart Cities Amelia Contesa; Pratiwi Rachmadi; Aziz Azindani
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.v1i1.18

Abstract

Smart cities are increasingly leveraging advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics to optimize urban management and improve the quality of life for citizens. However, managing vast and diverse datasets from numerous sources in real-time presents several challenges. This research proposes a modular framework that integrates distributed data processing engines with container-based workflow orchestration to address scalability, latency, adaptability, and fault tolerance in smart city data analytics. The framework utilizes cloud native technologies, including Apache Spark and Kubernetes, to efficiently manage resources and ensure high availability. The experimental setup tested the framework’s ability to handle dynamic data loads, demonstrating scalability through real-time resource allocation and low-latency processing. The adaptability of the framework was evident in its seamless integration with various data sources, such as environmental sensors and traffic management systems, which require different processing methods. Additionally, the framework’s modularity provided fault tolerance, enabling continued operation even if individual components failed, a crucial feature for mission-critical applications in smart cities. Compared to traditional monolithic systems, the proposed framework outperformed in flexibility, scalability, and performance, offering significant improvements in handling real-time data streams. Despite these advantages, challenges remain, particularly in integrating heterogeneous data formats and optimizing real-time processing for high-priority applications. The research highlights the importance of scalable data analytics and efficient workflow orchestration for the future of smart city platforms, offering a foundation for the development of more resilient, adaptable, and efficient cloud native infrastructures.
Application of Predictive Analytics Using Random Forest for Customer Churn Prediction on the Telco Customer Churn Dataset Amelia Contesa
Data Science Insights Vol. 4 No. 2 (2026): Journal of Data Science Insights
Publisher : PT. Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v4i2.234

Abstract

Predictive analytics is an important approach for supporting data-driven managerial decision-making, particularly in addressing customer churn, which can adversely affect company revenue. This study aims to develop a predictive customer churn model and generate insights that can be used to formulate customer retention strategies. The method employs the Random Forest algorithm implemented through RapidMiner for predictive modeling, while Tableau is used for data visualization and interactive exploration of churn patterns. The dataset used is the Telco Customer Churn dataset, which contains information on customer services, charges, and characteristics.The developed model achieved an accuracy of 79.21%, demonstrating a relatively good ability to identify customers who do not churn, although its ability to detect customers who are likely to churn remains limited. Further analysis indicates that the main factors associated with customer churn are contract type, service quality, monthly charges, and customer tenure. Visualization using Tableau reinforces these findings by showing that customers with month-to-month contracts, higher monthly charges, and shorter tenure have a higher risk of churn.The contribution of this study lies in integrating predictive modeling with interactive visualization to produce more comprehensive and accessible insights. This approach can help companies shift from reactive to proactive strategies for customer retention.
The Role Of Big Data Analytics In Strategic Decision-Making And Business Performance: A Systematic Literature Review Amelia Contesa; Ilzi Adrolis; Wenni Syafitri; Abdullah Abdullah
Business System & Innovation Journal Vol. 1 No. 2 (2026): Digital Innovation, Business Resilience, and Organizational Transformation
Publisher : Yayasan Fathurahman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67955/bsij.v1i2.18

Abstract

Digital transformation has increased organizational reliance on big data analytics (BDA) to support strategic decisions and improve business performance. This study synthesizes evidence on how BDA contributes to strategic decision-making, organizational performance, and innovation through a systematic literature review. The review followed the PRISMA 2020 framework and searched Scopus, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar for English-language journal and conference publications from 2021 to 2026. After identification, screening, and full-text eligibility assessment, 33 studies were included and examined using thematic analysis. The findings show that BDA strengthens decision quality and speed by combining analytics capability, predictive modeling, artificial intelligence, and data-driven insights. BDA is also associated with operational efficiency, project success, organizational agility, customer personalization, competitive advantage, sustainability, and innovation capability. The dominant themes were strategic decision-making, business performance, sustainability and innovation, and artificial intelligence with predictive analytics. However, the literature provides limited evidence on explainable and ethical artificial intelligence, human-AI collaboration, real-time analytics, and BDA adoption among small and medium-sized enterprises and organizations in developing economies. The review contributes an integrated view of BDA as a socio-technical and strategic capability and recommends transparent, scalable, and human-centered analytics governance.
Co-Authors Abdullah Abdullah Aziz Azindani Ilzi Adrolis Pratiwi Rachmadi Wenni Syafitri