Computer Systems Lab
Universitas Indonesia

Propelling the next era of breakthroughs in Computer Systems and Networks, from Big Data and High-Performance Computing,
to Real-Time System Infrastructures, through advanced algorithms, protocols, and frameworks that drive real-world innovation.

Big Data and AI Infrastructures

Dr. Ari Wibisono led a research project on big data and AI infrastructures dedicated to designing scalable, high-performance systems for data-intensive applications. The work advances real-time processing, intelligent inference, and efficient data management to address the growing volume, variety, and velocity of modern datasets. By developing flexible, robust infrastructures, the project enables seamless AI integration, fosters advanced analytics, and supports knowledge discovery across dynamic environments, providing a strong foundation for emerging scientific and practical applications.

    Grants
  1. Ari Wibisono (Co-PI), Intelligent Vehicle Body Damage Inspection System Using Computer Vision for Insurance Claims and Automotive Industry Applications (2025). Scheme: PT-LP, Rp. 338.060.000,-
  2. Petrus Mursanto (PI), Diffusion Model Imputation (2024). Scheme: PUTI Q1, Rp. 150.000.000,-
  3. Ari Wibisono (PI), Big Data Application (2023). Scheme: PUTI Q1, Rp. 120.000.000,-
  4. Petrus Mursanto (PI), Evaluation Criterion Enhancement of Generative Adversarial Network Imputation (2023). Scheme: PUTI Q1, Rp. 150.000.000,-
    Publications
  1. Wibisono, A., & Rahmadika, R. N. (2025). Comparative Evaluation of Database Systems for High-Volume Seismic Prediction Data Management in Real-Time Applications. Jurnal Ilmu Komputer dan Informasi, 18(2), 251-259.
  2. Wibisono, A., Denny., Mursanto, P., See, S. (2024). Natural Generative Noise Diffusion Model Imputation. Knowledge-Based Systems, vol. 301, 112310.
  3. Mursanto, P., Wibisono, A., Fahira, P. K., Rahmadhani, Z. P., & Wisesa, H. A. (2023). In-TFK: a scalable traditional food knowledge platform, a new traditional food dataset, platform, and multiprocess inference service. Journal of Big Data, 10(1), 47.

Blockchain Research and Applications

Dr. Setiadi Yazid led a blockchain research and applications project aimed at advancing the scalability, efficiency, and adaptability of blockchain technologies through both foundational and applied research. The project explores architectural enhancements, consensus mechanisms, and integration with emerging digital infrastructures. By tackling challenges such as scalability and interoperability, it supports the development of robust blockchain solutions across finance, supply chains, data integrity, and decentralized systems, while providing a forward-looking platform for continued innovation in the evolving blockchain ecosystem.

  1. Purnaadi, C. W., & Yazid, S. (2024). Sidechain implementation strategies to improve blockchain scalability. In Proceedings of AIP Conference, vol. 2920, no. 1, p. 020005. AIP Publishing LLC.

Wireless and Internet Technologies

Dr. Made Harta Dwijaksara led a research project on wireless and internet technologies aimed at enhancing the performance, reliability, and adaptability of modern communication systems through innovative protocol design and intelligent network optimization. The project explores congestion control in wireless environments, next-generation internet protocol enhancements, topology mapping of large-scale networks, and resource management in dynamic, spectrum- and bandwidth-constrained settings. By leveraging emerging approaches such as deep reinforcement learning and advanced wireless transport protocols, it addresses critical challenges in scalability, interoperability, and quality of service, establishing a robust platform for continued research and innovation in wireless communications and internet-based distributed systems.

    Grants
  1. Made Harta Dwijaksara (PI), A Lightweight Authentication Protocol for Modbus RTU (2024). Scheme: CSUI Grant, Rp. 49.110.000,-
  2. Muhammad Hilman (PI), Development of an Active Learning-Based QUIC Module for Teaching the OSI Transport Layer (2023). Scheme: CSUI Grant, Rp. 49.110.000,-
  3. Made Harta Dwijaksara (PI), Performance Enhancement of QUIC Protocol to Support High-Bandwidth Applications (2023). Scheme: CSUI Grant, Rp. 50.000.000,-
  4. Made Harta Dwijaksara (PI), Cell Selection Scheme Considering Carrier Aggregation and Dual Connectivity in Multi-RAT Cellular Networks Environment (2023). Scheme: UI-MMU BISA, Rp. 75.000.000,-
    Publications
  1. Dwijaksara, M. H., Darmawan, I. B. S., Angelina, M. A., Partogi, S. R., Arfiansyah, L., & Wibisono, A. (2025). Low Overhead Wi-Fi Fingerprinting-based Indoor Positioning for Evacuation Support System during Disaster in Smart Campus. In Proceedings of 2025 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), pp. 181-187. IEEE.
  2. Dwijaksara, M. H., Safaraz, R. T., Asyraf, M., Nugroho, J. P. R., & Thiagarajah, S. P. (2024). A Practical Indoor Positioning System Based on Collaborative PDR and Wi-Fi Fingerprinting. In Proceedings of 2024 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA). pp. 507-512. IEEE.
  3. Syukri, N. A. A. B., Thiagarajah, S. P., Dwijaksara, M. H., Alias, M. Y., & Lee, I. E. (2024). Green Dual Connectivity Selection Algorithm in 5G-NR/4G-LTE NSA HetNet. In Proceedings of 2024 IEEE 7th International Symposium on Telecommunication Technologies (ISTT), pp. 150-155. IEEE.
  4. Dwijaksara, M. H., Hilman, M. H., Lee, C., & Lee, W. (2024). Rate Adaptation Technique for Media Streaming Over QUIC With Limited Backhaul. IEEE Access.
  5. Witono, T., Yazid, S., & Sucahyo, Y. G. (2024). The Art of Internet Mapping: A Comprehensive Guide to Regional Internet Topology Mapping at the Autonomous System Level. Journal of Information Systems Engineering and Business Intelligence, 10(2), 191-205.
  6. Naqvi, H. A., Hilman, M. H., & Anggorojati, B. (2023). Implementability improvement of deep reinforcement learning based congestion control in cellular network. Computer Networks, 233, 109874.
  7. Naqvi, H. A., Hilman, M. H., & Anggorojati, B. (2023). Fine Tuning of Interval Configuration for Deep Reinforcement Learning Based Congestion Control. Jurnal Ilmu Komputer dan Informasi, 16(2), 151-161.

High Performance Computing

Prof. Heru Suhartanto led a high-performance computing (HPC) research project dedicated to developing scalable, efficient, and accessible solutions for complex computational challenges across diverse domains. The project explored containerized environments for parallel programming and applied HPC techniques to large-scale optimization problems such as disaster response and resource planning. Emphasizing both infrastructure advancement and real-world applications, it supports scientific discovery, high-demand simulations, and data-intensive processing, providing a strong foundation for continued innovation in computational science.

    Grants
  1. Heru Suhartanto (Co-PI), CARE-Net: Optimization of Medical Language Models and Parallel Inference for Automated Clinical Reporting on High-Performance Computing Platforms (2025). Scheme: PPS-PDD, Rp. 52.210.000,-
    Publications
  1. Arisal, A., & Suhartanto, H. (2024). HPC-in-Containers: A Containerized Parallel Environment for Parallel Programming Learning Using Docker. In Proceedings of 2024 International Conference on Information Technology Systems and Innovation (ICITSI), pp. 101-105. IEEE.
  2. Tarhan, İ., Zografos, K. G., Sutanto, J., Kheiri, A., & Suhartanto, H. (2023). A multi-objective rolling horizon personnel routing and scheduling approach for natural disasters. Transportation research part C: emerging technologies, 149, 104029.

Multi-tenant Container Orchestration

Dr. Muhammad Hilman led a multi-tenant container orchestration research project focused on developing intelligent, scalable, and adaptive strategies for managing containerized applications in cloud-native environments. The project advanced techniques for dynamic resource allocation, status-aware scaling, and efficient microservice orchestration to ensure performance, reliability, and cost-efficiency across multiple tenants. By addressing challenges such as workload variability, elasticity, and system heterogeneity, it lays the foundation for next-generation orchestration platforms that enable seamless, resilient, and autonomous operations at scale.

    Grants
  1. Muhammad Hilman (PI), Microservices Orchestration for Multi-tenant Software-as-a-Service Environment (2025). Scheme: PUTI Q1, Rp. 149.990.000,-
    Publications
  1. Wen, L., Xu, M., Gill, S. S., Hilman, M. H., Srirama, S. N., Ye, K., & Xu, C. (2025). StatuScale: Status-aware and elastic scaling strategy for microservice pplications. ACM Transactions on Autonomous and Adaptive Systems, 20(1), 1-25.

Real-Time Streaming Infrastructure

Dr. Muhammad Hilman led a real-time streaming infrastructure research project focused on developing low-latency, high-reliability systems for dynamic and distributed environments. The project advanced real-time data transmission, adaptive streaming, and edge-assisted processing to support applications such as remote sensing, autonomous systems, and UAV operations. By addressing challenges including mobility, signal variability, and responsiveness, it created robust, flexible frameworks capable of efficient operation in complex, rapidly changing conditions, laying the groundwork for future innovations in real-time, data-driven technologies.

    Grants
  1. Muhammad Hilman (PI), Real-time Search and Rescue (SAR) Monitoring and Surveilance using UAVs and Fog Computing Platform (2023). Scheme: UI-MMU BISA, Rp. 75.000.000,-
  2. Muhammad Hilman (PI), FogVerse: Kafka-based Fog Computing and Resource Management Platform (2023). Scheme: CSUI Grant, Rp. 49.950.000,-
    Publications
  1. Bakhuraisa, Y., Lim, H. S., Chan, Y. K., & Hilman, M. H. (2025). UAV-Assisted Localization of Ground Nodes in Urban Environments Using Path Loss Measurements. Drones, 9(6), 450.