Zero to Win Algoritma Rekursif sebagai Otak Strategis Permainan Tic Tac Toe

Authors

  • Andini Widi Bahrani Universitas Bina Darma
  • Nabila Ade Mutmaina Universitas Bina Darma
  • Tata Sutabri Universitas Bina Darma

DOI:

https://doi.org/10.51903/f74gjm69

Keywords:

Artificial intelligence, Tic Tac Toe, recursive algorithm, optimal strategy, Minimax

Abstract

Tic Tac Toe is a classic game that, despite its simplicity, presents a compelling structure for computational strategy analysis. This study aims to develop an artificial intelligence system capable of playing optimally using a recursive algorithmic approach. By implementing the Minimax algorithm, the system evaluates all possible moves and outcomes in each game state, allowing it to choose the most strategic action to secure a win or avoid a loss. The approach was tested through simulations against both human and computer opponents, showing that the recursive strategy effectively generates fast and accurate decisions. The results highlight that recursive algorithms can significantly enhance the performance of intelligent systems in solving deterministic, rule-based games.

References

1. Russell, Stuart, and Peter Norvig. Artificial Intelligence: A Modern Approach. 3rd ed., Pearson, 2016.

2. Nilsson, Nils J. Artificial Intelligence: A New Synthesis. Morgan Kaufmann, 1998.

3. Pearl, Judea. Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley, 1984.

4. Luger, George F. Artificial Intelligence: Structures and Strategies for Complex Problem Solving. 5th ed., Pearson Education, 2005.

5. Sedgewick, Robert, and Kevin Wayne. Algorithms. 4th ed., Addison-Wesley, 2011.

6. Chen, Wei, and Jie Tang. “Efficient Implementation of Minimax Algorithm with Alpha-Beta Pruning.” Journal of Intelligent & Fuzzy Systems, vol. 33, no. 2, 2017, pp. 1045–1053. doi:10.3233/JIFS-169530.

7. Bjoernson, Richard, and Charles Thayer. “Recursive Minimax Algorithm for Two-Player Games.” International Journal of Computer Science and Information Technology, vol. 11, no. 4, 2019, pp. 45–53.

8. He, Xiaoyu, and Tian Xie. “AI for Board Games: From Tic Tac Toe to Chess.” Proceedings of the 2018 International Conference on Artificial Intelligence and Computer Engineering, 2018, pp. 112–119.

9. Python Software Foundation. Python 3.11 Documentation. 2023. https://docs.python.org/3/.

Accessed 30 May 2025.

10. Zobrist, Albert L. “A New Hashing Method with Application for Game Playing.” Technical Report, 1970.

11. Aho, Alfred V., Jeffrey D. Ullman. The Design and Analysis of Computer Algorithms. Addison-Wesley, 1974.

12. Knuth, Donald E., and Ronald W. Moore. “An Analysis of Alpha-Beta Pruning.” Artificial Intelligence, vol. 6, no. 4, 1975, pp. 293–326.

13. Wikipedia contributors. “Minimax.” Wikipedia, The Free Encyclopedia, 2025, https://en.wikipedia.org/wiki/Minimax. Accessed 30 May 2025.

14. Wikipedia contributors. “Tic-tac-toe.” Wikipedia, The Free Encyclopedia, 2025, https://en.wikipedia.org/wiki/Tic-tac-toe. Accessed 30 May 2025.

15. Sharma, R., and Singh, P. “Game Theory and Artificial Intelligence: Minimax Algorithm.”

International Journal of Advanced Research in Computer Science and Software Engineering, vol. 7, no. 2, 2017, pp. 32–36.

16. Campbell, Murray, A. Joseph Hoane Jr., and Feng-hsiung Hsu. “Deep Blue.” Artificial Intelligence, vol. 134, no. 1–2, 2002, pp. 57–83.

17. Silva, Eduardo, and Leandro Nunes de Castro. Computational Intelligence: Principles and Practice. Wiley-IEEE Press, 2007.

18. Berlekamp, Elwyn R., John H. Conway, and Richard K. Guy. Winning Ways for Your Mathematical Plays. Academic Press, 1982.

19. Winston, Patrick H. Artificial Intelligence. 3rd ed., Addison-Wesley, 1992.

20. Veness, Joel. Artificial Intelligence for Games. CRC Press, 2012.

21. Baharani, A. W., Apriza, Z., Mutmaina, N. A., & Sutabri, T. (2024). Perbandingan kinerja mata uang kripto utama: Bitcoin vs Ethereum. International Journal of Management, 2(1). https://doi.org/10.55927/ijm.v2i1.568

22. Mutmaina, N. A., & Sutabri, T. (2025). Sistem pembelajaran multimedia dengan teknologi voice recognition untuk berkebutuhan khusus. Jurnal Sains Student Research (JSSR), 3(2), 470–473. https://doi.org/10.61722/jssr.v3i2.4342Journal+2

23. Mutmaina, N. A., Bahrani, A. W., Apriza, Z., & Sutabri, T. (2023). Penerapan teknologi informasi untuk pengembangan bisnis dropship. IJM: Indonesian Journal of

Multidisciplinary, 1(5). Retrieved from https://journal.csspublishing.com/index.php/ijm/article/view/412

24. Bahrani, A. W., & Sutabri, T. (2025). Strategi pengembangan startup digital di Indonesia: Menumbuhkan inovasi, meningkatkan kualitas, dan memperluas aksesibilitas untuk generasi muda. IJM: Journal of Multidisciplinary, 3(2), 123-135. https://ojs.csspublishing.com/index.php/ijm/article/view/142

25. Billan, A. C., & Sutabri, T. (2025). Restorasi Penjadwalan Sumur Minyak yang Mengalami Off-Time Menggunakan Algoritma Backtracking dalam Upaya Optimasi Produksi. Bulletin of Computer Science Research, 5(3), 228–234. https://doi.org/10.47065/bulletincsr.v5i3.507

26. Pratama, Y. H., & Sutabri, T. (2023). Analisis Kriptografi Algoritma Blowfish pada Keamanan Data Menggunakan Dart. Jurnal Informatika Terpadu, 9(2), 126–135. https://doi.org/10.54914/jit.v9i2.975

27. Sutabri, T., & Napitupulu, D. (n.d.). Sistem informasi bisnis. Yogyakarta: Penerbit Andi.

28. Sutabri, T. (n.d.). Konsep sistem informasi. Yogyakarta: Penerbit Andi.

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Published

2025-05-01

How to Cite

Zero to Win Algoritma Rekursif sebagai Otak Strategis Permainan Tic Tac Toe. (2025). Jurnal Manajemen Informatika & Teknologi, 5(1), 31-45. https://doi.org/10.51903/f74gjm69

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