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All Journal Jurnal Simetris Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JOIV : International Journal on Informatics Visualization Emerging Science Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknologi Informasi MURA Jurnal Teknologi Sistem Informasi dan Aplikasi Journal of Information Systems and Informatics APTIKOM Journal on Computer Science and Information Technologies (CSIT) IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences Jurnal Locus Penelitian dan Pengabdian Jurnal Bina Komputer Dikdimas : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi Mura Journal of Current Research in Blockchain International Journal Research on Metaverse
Kurniawan, Tri Basuki
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Nursing Social Sciences Other

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Journal : Journal of Current Research in Blockchain

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Predicting Throughput and Latency in Hyperledger Fabric Blockchains Using Random Forest Regression Dewi, Deshinta Arrova; Kurniawan, Tri Basuki
Journal of Current Research in Blockchain Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i1.27

Abstract

The study focuses on enhancing the performance optimization of Hyperledger Fabric blockchains through predictive modeling using Random Forest regression. It emphasizes the importance of accurately predicting two critical performance metrics—throughput (measured in transactions per second or TPS) and latency (defined as the time taken to confirm transactions). These metrics directly influence the efficiency and user experience of blockchain applications, making their accurate prediction essential for configuring blockchain networks effectively. The research leverages data collected through Hyperledger Caliper, a benchmarking tool, which provides detailed measurements of various configuration parameters, including block size, transaction arrival rate, and the number of orderer nodes. Through rigorous exploratory data analysis, the study identifies how these parameters impact throughput and latency, revealing complex interdependencies that challenge traditional optimization approaches. Using Random Forest regression, a robust ensemble learning method, the study demonstrates that the predictive model can achieve high accuracy. The performance of the model is assessed using metrics such as R-squared values, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), which collectively underscore its ability to offer reliable predictions across varying configurations. The results of this research provide practical insights for blockchain administrators, allowing them to configure Hyperledger Fabric settings more efficiently, thereby reducing the trial-and-error process typically involved in performance tuning. Moreover, the study's findings contribute to the broader field of blockchain performance optimization by offering a data-driven framework that bridges theoretical analysis with practical application in real-world scenarios. Looking forward, the study suggests avenues for future research, including expanding the dataset to cover more diverse blockchain platforms and configurations, incorporating real-world deployment data for validation, and exploring additional machine learning algorithms for even greater predictive accuracy. This approach highlights the critical role of data-driven methodologies in optimizing blockchain network performance and encourages further collaboration and exploration in the domain.
Co-Authors - Kurniawan, - Adi Wijaya Agus Riyanto Alde Alanda, Alde Alqudah, Mashal Kasem Alqudah, Musab Kasim Andri Andri Antoni, Darius Armoogum, Sheeba Armoogum, Vinaye Asro, Asro Astried, Astried Aziz, RZ. Abdul Azmi, Nurhafifi Binti Bappoo, Soodeshna Batumalay, Malathy Bidul, Winarsi J. Bujang, Nurul Shaira Binti Chandra, Anurag Dedy Syamsuar Dewi, Deshinta Arrova Dewi, Deshinta Arrowa Diana Diana Edi Surya Negara Eko Risdianto Fadly Fadly Fatoni, Fatoni Febriyanti Panjaitan Firosha, Ardian Fuad, Eyna Fahera Binti Eddie Habib, Shabana Hadi Syahputra Hanan, Nur Syuhana binti Abd Hasibuan, M.S. Henderi . Hendra Kurniawan Herdiansyah, M. Izman Hidayani, Nieta Hisham, Putri Aisha Athira binti Irianto, Suhendro Y. Irwansyah Irwansyah Ismail, Abdul Azim Bin Isnawijaya, Isnawijaya Joan Angelina Widians, Joan Angelina Kijsomporn, Jureerat Kurniawan, Dendi Lexianingrum, Siti Rahayu Pratami M Said Hasibuan Madjid, Fadel Muhammad Maizary, Ary Mantena, Jeevana Sujitha Mashal Alqudah Melanie, Nicolas Misinem, Misinem Mohd Salikon, Mohd Zaki Motean, Kezhilen Muhamad Akbar Muhammad Islam, Muhammad Muhammad Nasir Muhayeddin, Abdul Muniif Mohd Nathan, Yogeswaran Nazmi, Che Mohd Alif Oktariansyah Oktariansyah, Oktariansyah Onn, Choo Wou Periasamy, Jeyarani Prahartiningsyah, Anggari Ayu Pratiwi, Ayu Okta Praveen, S Phani Puspitasari, Novianti Qisthiano, M Riski R Rizal Isnanto Rahmi Rahmi RR. Ella Evrita Hestiandari Saksono, Prihambodo Hendro Saringat, Zainuri Singh, Harprith Kaur Rajinder Sirisha, Uddagiri Sri Karnila Sugiyarto Surono, Sugiyarto Sulaiman, Agus Sunda Ariana, Sunda Suriani, Uci Syaputra, Hadi Taqwa, Dwi Muhammad Thinakaran, Rajermani Triloka, Joko Udariansyah, Devi Usman Ependi Wibaselppa, Anggawidia Yeh, Ming-Lang Yesi Novaria Kunang Yorman Yupika Maryansyah, Yupika Yusuf, Abi daud Zakari, Mohd Zaki Zakaria, Mohd Zaki