Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal Journal of Applied Data Sciences Journal of Current Research in Blockchain
Tri Basuki Kurniawan
Faculty of Science Technology, Universitas Bina Darma, Palembang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Predicting Throughput and Latency in Hyperledger Fabric Blockchains Using Random Forest Regression Deshinta Arrova Dewi; Tri Basuki Kurniawan
Journal of Current Research in Blockchain Vol. 2 No. 1 (2025): Regular Issue March 2025
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.
Gum Disease Identification Using Fuzzy Expert System Muhammad Nasir; Tri Basuki Kurniawan; Deshinta Arrova Dewi; Mohd Zaki Zakaria; Nurul Shaira Binti Bujang
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.346

Abstract

Gum disease, including Gingivitis and Periodontitis, is among the most common dental conditions, primarily caused by dental plaque, a bacterial biofilm. These conditions are strongly linked to various systemic illnesses, including cancer, atherosclerosis, hypertension, stroke, and respiratory and cardiovascular conditions like aspiration pneumonia, as well as adverse pregnancy outcomes. Gum inflammation is typically characterized by symptoms such as increased redness, swelling (edema), and a loss of surface texture (stippling; gum fiber attachment). These symptoms are site-specific, meaning that an individual can have both healthy and diseased areas within their mouth. In this research, we developed a fuzzy expert system using MATLAB to identify gum diseases. The system was tested on various cases and produced an output value of 0.133, which successfully identified Gingivitis. This value was derived using a fuzzy logic system that processes input data through predefined rules within the Fuzzy Expert System (FES). The system utilizes several input variables such as the frequency of gum bleeding, the extent of plaque accumulation, the depth of gum recession, and the degree of tooth mobility. The key contribution of this study lies in the integration of fuzzy logic to handle the inherent uncertainties in clinical diagnosis, providing a more nuanced assessment compared to traditional methods. The novelty of this research is the application of a fuzzy expert system in dental diagnostics, offering a promising tool for improving the accuracy and efficiency of gum disease identification in clinical settings. This system has the potential to assist dentists in making more informed decisions, ultimately leading to better patient outcomes.
Co-Authors Deshinta Arrova Dewi Deshinta Arrova Dewi Mohd Zaki Zakaria Muhammad Nasir Nurul Shaira Binti Bujang