Analysis of the Monte Carlo Method in Simulation of Snake and Ladder Game Using R Programming

Afif Yasri; Ramlan Marbun; Ade May Luky Harefa; Muhammad Syahputra Novelan

Authors

Afif Yasri Teknologi Informasi, Universitas Pembangunan Panca Budi, Jl. Jendral Gatot Subroto Km. 4,5, Medan, Indonesia
Ramlan Marbun Teknologi Informasi, Universitas Pembangunan Panca Budi, Jl. Jendral Gatot Subroto Km. 4,5, Medan, Indonesia
Ade May Luky Harefa Teknologi Informasi, Universitas Pembangunan Panca Budi, Jl. Jendral Gatot Subroto Km. 4,5, Medan, Indonesia
Muhammad Syahputra Novelan Teknologi Informasi, Universitas Pembangunan Panca Budi, Jl. Jendral Gatot Subroto Km. 4,5, Medan, Indonesia

Keywords:

Monte Carlo, Simulation, Snakes and Ladders, R Programming, Probabilistic Modeling

Abstract

This study applies the Monte Carlo method to simulate the classic board game "Snakes and Ladders" using the R programming language. The research aims to explore how randomness and probability influence the number of moves needed to complete the game and to provide a statistical overview of game outcomes. A simulation of 10,000 iterations was conducted, where each iteration represents one complete game play, starting from position 1 and ending exactly at position 100. The results show that players require an average of 51.41 moves to finish the game, with a minimum of 8 and a maximum of 394 moves. These results illustrate the highly variable nature of the game due to random dice rolls and the presence of snakes and ladders that can significantly alter a player's position. Visualization techniques such as histograms, density plots, boxplots, and line graphs were used to represent the distribution and variability of moves. The findings demonstrate the effectiveness of Monte Carlo simulations in analyzing stochastic systems, where outcomes are driven by random variables. This research contributes to the understanding of probabilistic modeling and can serve as a simple yet insightful example of applying computational methods to real-world scenarios.

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References

Constantin, M. A., Schuurman, N. K., & Vermunt, J. K. (2023). A general Monte Carlo method for sample size analysis in the context of network models. Psychological Methods.

Dalimunthe, N. A. (2021). Simulasi Prediksi Permintaan Kue Kacang Di Cv. Fawas Jaya Medan Menggunakan Metode Monte Carlo. Universitas Medan Area.

Desi, E., Aliyah, S., Lubis, C. P., Nst, M. A. E., & Tahel, F. (2024). Simulasi Monte Carlo Dalam Meprediksikan Tingkat Lonjakan Pendaftaran Vaksin Booster Pada Puskesmas Martubung. Jurnal Teknologi Informasi Dan Ilmu Komputer, 11(3), 579–586.

Fitriani, L., Tresnawati, D., & Pamungkas, M. I. I. S. (2023). Multiplayer Game Guessing Sunda’s Proverb Using Socket. Io And Node. Js. JUITA: Jurnal Informatika, 11(2), 231–240.

Kabacoff, R. (2022). R in action: data analysis and graphics with R and Tidyverse. Simon and Schuster.

Signoret, J.-P., Leroy, A., Signoret, J.-P., & Leroy, A. (2021). Monte Carlo Simulation. Reliability Assessment of Safety and Production Systems: Analysis, Modelling, Calculations and Case Studies, 547–586.

Sukarsa, I. M., Bayupati, I. P. A., Ananda, G. O. D., Buana, P. W., & Wisswani, N. W. (2023). An Efficient Synchronization Model in Random Word Educational Games with Multiplayer Platforms. Journal of Advances in Information Technology, 14(5).

Syahbana, H., Akbar, M. F., Syahputra, M., & Efriyanti, L. (2023). Pemodelan Decision Modeling on Clothing Model Selection of Female Students of UIN Bukittinggi. SMART: Jurnal Teknologi Informasi Dan Komputer, 2(2), 93–108.

Wali, M., Nengsih, T. A., Hts, D. I. G., Choirina, P., Awaludin, A. A. R., Yusuf, M., Aminuddin, F. H., Purwandari, N., & Baradja, A. (2023). Pengantar 15 Bahasa Pemrograman Terbaik Di Masa Depan (Referensi & Coding Untuk Pemula). PT. Sonpedia Publishing Indonesia.

Wati, D. C., Judijanto, L., Apriyanto, A., Sepriano, S., & Maryana, M. (2024). Media & Teknologi Pembelajaran Matematika. PT. Sonpedia Publishing Indonesia.