Penerapan Simulasi Monte Carlo untuk Memprediksi Kinerja dan Keandalan Sistem Komputer Berbasis Artificial Intelligence
DOI:
https://doi.org/10.63822/4dv3rv27Keywords:
Monte Carlo Simulation; Artificial Intelligence (AI); AI-Based Computer System Reliability.Abstract
The development of Artificial Intelligence (AI)-based computer systems in large-scale data processing environments introduces significant uncertainty regarding operational success rates. AI systems handling thousands to millions of processing requests daily are vulnerable to failures caused by workload variations, limited computational capacity, and model complexity. This study applies a quantitative approach using Monte Carlo Simulation to predict the performance and reliability of AI-based computer systems in data processing by utilizing probability concepts, discrete probability distributions, cumulative probabilities, expected value analysis, and Mean Absolute Percentage Error (MAPE). The research data consist of 250 historical processing observations classified into four categories: optimal success (58%), delayed success (24%), temporary failure with retry (12%), and total failure (6%). The expected processing time was calculated as E(X) = 3.816 seconds. A Monte Carlo Simulation with 10,000 iterations produced an average MAPE of 0.58%, indicating excellent predictive accuracy since it is well below the 5% tolerance threshold. Furthermore, under a workload of 50,000 requests per day, approximately 8,950 requests (17.9%) were estimated to potentially affect service reliability. The findings demonstrate that Monte Carlo Simulation is an effective quantitative tool for capacity planning and operational risk mitigation in AI-based computer systems.
References
Abdi, K., & Lubis, M. R. A. (2024). Prediksi Jumlah Mahasiswa Baru FMIPA UNIMED Dengan Menggunakan Teknik Simulasi Monte Carlo. Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer, 2(3), 72–83.
Asril, A. P. (2022). Simulasi dalam Menganalisis Tingkat Pendapatan Penjualan Produk Bengkel Las menggunakan Metode Monte Carlo. Jurnal Sistim Informasi Dan Teknologi, 5, 7–9. https://doi.org/10.37034/jsisfotek.v5i1.155
Bayu Viargo, A. K., Saifudin, T., & Chamidah, N. (2023). Prediksi Jumlah Penumpang Kereta Api Stasiun Surabaya Gubeng dengan Metode Monte Carlo. Limits: Journal of Mathematics and Its Applications, 20(3), 275. https://doi.org/10.12962/limits.v20i3.16123
Goyal, H. (2025). Artificial Intelligence for Cost-Aware Resource Prediction in Big Data Pipelines. http://arxiv.org/abs/2510.05127
Guesmi, H. A. (2024). Prediction of the Mean Time to Failures of a Complex System Using the Monte Carlo Simulation Method. Journal of Reliability and Statistical Studies, 17(2), 267–288. https://doi.org/10.13052/jrss0974-8024.1721
Julitz, T. M., Tordeux, A., Schlüter, N., & Löwer, M. (n.d.). Reliability of Redundant M-Out-Of-N Architectures With Dependent Components: A Comprehensible Approach With Monte Carlo Simulation.
Kayadibi, S. Y. (2025). Quantifying Student Success with Generative AI: A Monte Carlo Simulation Informed by Systematic Review. http://arxiv.org/abs/2507.01062
Listy, V., & Ilham, I. (2025). Revolusi Sistem Informasi Manajemen di Era AI dan Big Data Mengubah Cara Bisnis Bekerja. Simpatik: Jurnal Sistem Informasi Dan Informatika, 5(1), 27–36. https://doi.org/10.31294/simpatik.v5i1.7621
Maspul, K. A., & Putri, N. K. (2025). Big Data and Predictive Analytics for Indonesia’s Economic Transformation and Digital Resilience. Journal of Technology and System Information, 2(2), 22. https://doi.org/10.47134/jtsi.v2i2.3774
Mustafian, M., Mauliddin, M., & Abdal, A. M. (2024). Penerapan Value-at-Risk dan Conditional-Value- at-Risk Dalam Pengukuran Risiko Portofolio Optimal Menggunakan Pendekatan Simulasi Monte Carlo. Jurnal Riset Dan Aplikasi Matematika (JRAM), 8(1), 39–50. https://doi.org/10.26740/jram.v8n1.p39-50
Nawawi, H. M., Hikmah, A. B., Mustopa, A., & Wijaya, G. (2024). Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir Era modern telah dipenuhi oleh kompleksitas pasar kerja yang terus berubah , maka dari itu penting bagi maupun organisasi untuk untuk mengatasi kompleksitas dan dalam pengambilan. 14(1), 13–25.
Pokorádi, L. (2024). Monte-Carlo Simulation of Reliability of System with Complex Interconnections. Vehicles, 6(4), 1801–1811. https://doi.org/10.3390/vehicles6040087
Rahmayana, R., Bachmid, S., & Maricar, H. (2025). Kajian pengendalian waktu dan biaya dengan metode nilai hasil dan simulasi monte carlo. Jurnal Teknik Industri Terintegrasi, 8(1), 1230–1238. https://doi.org/10.31004/jutin.v8i1.41834
Tzivrailis, D., Rosso, A., & Kawasaki, E. (2025). Uncertainty in AI-driven Monte Carlo simulations. 1–15. http://arxiv.org/abs/2506.14594
Yigit, Y., Ferrag, M. A., Sarker, I. H., Maglaras, L. A., Chrysoulas, C., Moradpoor, N., & Janicke, H. (2024). Critical Infrastructure Protection: Generative AI, Challenges, and Opportunities. 4. http://arxiv.org/abs/2405.04874
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Copyright (c) 2026 Augusta Dwi Putra, Bangkit Ririatini, Dzaki Dwi Abdullah, Rifqi Aziz, Purwadi Purwadi (Author)
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