PEMODELAN SIMULASI KINERJA JARINGAN KOMPUTER MENGGUNAKAN METODE MONTE CARLO UNTUK ANALISIS PERFORMA TRAFIK JARINGAN LOKAL

David  Jonathan Wijaya; Haikal  Azzikra Purwoko; Arami  Rizki Gunawan; Kevin  Rizki Fauzi; Andrea Ramadhan  Cahya Ardhana; Siti  Rihastuti

Authors

David Jonathan Wijaya STMIK AMIKOM Surakarta Author
Haikal Azzikra Purwoko STMIK Amikom Surakarta Author
Arami Rizki Gunawan STMIK Amikom Surakarta Author
Kevin Rizki Fauzi STMIK Amikom Surakarta Author
Andrea Ramadhan Cahya Ardhana STMIK Amikom Surakarta Author
Siti Rihastuti STMIK Amikom Surakarta Author

Keywords:

Monte Carlo, Network Performance, Latency, Throughput, Python

Abstract

This study proposes an enhanced Monte Carlo–based stochastic model specifically applied to the analysis of local network traffic performance. The novelty of this research lies in the integration of probabilistic traffic modeling with scenario-based validation to more accurately capture real-world variability. The simulation evaluates latency, throughput, and packet loss using 10,000 iterations, generating probabilistic performance distributions. The results show that the Monte Carlo method effectively represents dynamic patterns in local network traffic, yielding an average latency of 5.2 ms, throughput between 85–97 Mbps, and average packet loss of 1.8%. This research contributes an adaptable analytical framework that improves accuracy in evaluating local network performance under uncertain traffic conditions.

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References

Y. Hu, B. Liu, and J. Li, “Network Traffic Prediction in an Edge – Cloud Continuum Network for Multiple Network Service Providers,” 2024.

A. Bouillard, “Stochastic Network Calculus with Localized Application of Martingales,” pp. 1–29, 2024.

M. Klinkowski, “Performance Analysis of Data-Driven and Deterministic Latency Models in Dynamic Packet-Switched Xhaul Networks,” 2025.

F. I. Haq, A. H. Firmansyah, W. P. Jatmiko, and S. Agustin, “Implementasi Penggunaan Simulasi Monte Carlo dalam Estimasi,” vol. 7, no. 4, pp. 499–507, 2024.

V. Nomor, D. Chirzah, and H. M. Jumasa, “Strategi Pengelolaan Bandwidth Adaptif Pada Jaringan Komputer Berbasis Virtualisasi,” vol. 8, pp. 33–43, 2025.

J. Toutouh, S. Nesmachnow, J. Toutouh, and S. Nesmachnow, “Evolutionary Power-Aware Routing in VANETs using Monte-Carlo Simulation Evolutionary Power-Aware Routing in VANETs using Monte-Carlo Simulation,” pp. 119–125, 2012, doi: 10.1109/HPCSim.2012.6266900.Evolutionary.

P. Y. Omole-matthew, G. J. Arome, A. F. Thompson, and B. K. Alese, “Monte Carlo Simulation Approach to Network Access Control,” vol. 9, no. 1, pp. 726–729, 2021, doi: 10.20533/jitst.2046.3723.2021.0088.

H. Bege, A. Y. Zubairu, and M. Carlo, “Campus realities : forecasting user bandwidth utilization using Monte Carlo simulation,” vol. 10, no. 5, pp. 4809–4817, 2020, doi: 10.11591/ijece.v10i5.pp4809-4817.

Y. Chen et al., “Statistical QoS Provisioning Analysis and Performance Optimization in xURLLC-enabled Massive MU-MIMO Networks : A Stochastic Network Calculus Perspective,” pp. 1–35.

S. Metode, M. Carlo, and S. Antrian, “Jurnal KomtekInfo,” vol. 11, pp. 149–156, 2024, doi: 10.35134/komtekinfo.v11i3.552.

M. Fajri, F. Ilmu, K. Universitas, and M. Buana, “SIMULASI ANTRIAN PAKET DATA JARINGAN DENGAN MEKANISME DROP TAIL,” vol. VIII, no. 2, pp. 151–160, 2016.

J. O. Holman and A. Hacherl, “Teaching Monte Carlo Simulation with Python Teaching Monte Carlo Simulation with Python ABSTRACT,” J. Stat. Data Sci. Educ., vol. 0, no. 0, pp. 1–18, 2022, doi: 10.1080/26939169.2022.2111008.

G. Bouloukakis, I. Moscholios, N. Georgantas, and V. Issarny, “Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models,” pp. 1–13, 2021.

M. Monte, C. Sebagai, and S. Berbasis, “KETIDAKPASTIAN DALAM BERBAGAI MULTIDISIPLIN,” pp. 120–134.

U. Achlison, J. T. Santoso, K. Rozikin, and F. Diapoldo, “Analisis Latensi Video Streaming Antara Jaringan Berbasis Local Area Network dan Web,” vol. 15, no. 2, pp. 473–477, 2022.