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All Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics G-Tech : Jurnal Teknologi Terapan
Gilang Raka Rayuda Dewa
Sampoerna University, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy Engineering

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Performance Analysis of Synchronous Multilink in Wireless-Based Computer Networks Dewa, Gilang Raka Rayuda
ILKOMNIKA Vol 7 No 2 (2025): Volume 7, Number 2, August 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i2.778

Abstract

The increasing demand for data transmission results in throughput degradation, which lowers the data rate and increases network outages. ITU notes that global mobile broadband surpasses 1ZB and continues to grow in successive years. Numerous techniques have been investigated to maintain the expected throughput with low computational complexity, including the synchronous multilink method. This technique generates multiple data links to enable simultaneous transmission, allowing for the transmission of more data. However, there is no unified analytical model that captures the inherent trade-offs with procedural simulation in multilink operations. Accordingly, this paper provides a comprehensive analysis of synchronous multilink. The analysis includes the work system, constraints, mathematical expression, Markov chain model, and performance result of synchronous multilink. The simulation results indicate that the synchronous multilink offers promising performance, albeit with certain limitations, for wireless-based computer networks.
Advanced Machine Learning Techniques for Assessing Water Quality: A Comparative Study Using Ensemble, Neural Networks, and Instance-Based Models Muhammad Hafiz; Johan Iswara; Bari Fakhrudin; Widitra Nararya Rama; Avellino Vincent Juwono; Gilang Raka Rayuda Dewa
G-Tech: Jurnal Teknologi Terapan Vol 9 No 3 (2025): G-Tech, Vol. 9 No. 3 July 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i3.7162

Abstract

Access to safe water remains a significant issue, with around 5.8 billion people lacking access to potable water globally. Rapid and accurate identification of water safety is thereby essential to reduce public waterborne diseases. However, conventional laboratory-based testing is typically time-consuming and expensive. On the other hand, machine learning provides time- and cost-effective assessments based on physicochemical properties. Unfortunately, most studies only evaluate a single model type in a small dataset, resulting in limited insight that makes it hard to determine the actual effectiveness of these models. To address this limitation, the present study conducts a comparative analysis of three machine learning paradigms: ensemble-based, neural network-based, and instance-based models. Using a publicly available dataset of 7,999 samples, each model is evaluated using key performance metrics, including accuracy, precision, and confusion matrix analysis. The evaluation results show that the ensemble-based model achieves the highest accuracy of 96.62% and precision of 96.53%, outperforming the neural network-based model, which achieves an accuracy of 94.75% and precision of 70.47%. Additionally, the instance-based model achieves an accuracy of 91.12% and a precision of 83.04%. These results indicate the effectiveness of the ensemble-based model for real-time water quality monitoring.
Co-Authors Avellino Vincent Juwono Bari Fakhrudin Johan Iswara Muhammad Hafiz Widitra Nararya Rama