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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Big Data Analytics and Data Science
Pratiwi Rachmadi
Perbanas Institute
Computer Science & IT Economics, Econometrics & Finance

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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.
Kompleksitas Proses dan Non-Konformitas Sebagai Faktor Keterlambatan Pada Alur Kerja Purchase-To-Pay Lengkap Pendekatan Machine Learning Lucia Sri Istiyowati; Pratiwi Rachmadi; Nani Krisnawaty Tachjar
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 2 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No2.pp801-809

Abstract

Invoice processing is a crucial component of a financial information system, particularly the accounts payable module integrated with an Enterprise Resource Planning (ERP) system. Delays in invoice processing are common, leading to financial penalties. This study employs a data-driven approach by integrating process mining and machine learning using the XGBoost algorithm to analyze the factors contributing to delays in the purchase-to-pay (P2P) workflow. Event log data from the BPI Challenge 2019 public dataset and refined to 3,471 complete cases. Process discovery and performance analysis were performed using the Disco tool, while an XGBoost binary classification model was developed with 16 process-level features extracted from the event logs. The model yielded an AUC-ROC of 0.6267 with a 5-fold cross-validation of 0.6382 and a standard deviation of 0.0099, indicating moderate discriminatory ability. Feature importance analysis identified process complexity (0.191), ideal workflow conformity (0.187), and invoice cancellation frequency (0.122) as the dominant delay factors, while document completeness contributed insignificantly. These findings suggest that in a complete P2P workflow, the cause of delay lies in the characteristics of process execution. The results of this study provide direction for developing accounts payable information systems with a focus on complexity reduction and real-time compliance monitoring.
Co-Authors Amelia Contesa Aziz Azindani Lucia Sri Istiyowati Nani Krisnawaty Tachjar