Meningkatkan Variasi Tindakan Non-Playable Character Pada Game Survival Menggunakan Metode Markov

Authors

  • Hendra Winata Program Studi Informatika
  • Liliana Liliana Program Studi Informatika
  • Hans Juwiantho Program Studi Informatika

Keywords:

Government Public Relations, Analisis Isi, Kementerian Republik Indonesia, E-Government

Abstract

Digital games or often called Video games are common today. The development of game variants makes games never stop improving, especially in the Artificial Intelligence section. Each game has its own artificial intelligence so that many variations are generated and make a game unique. This research tries to make a variation of the actions taken by NPCs against players. In an effort to make these variations, the Markov Chain method is used to help state selection. Markov Chain method is combined with Finite-State Machine for NPC state selection. Based on the results of testing and questionnaires, 80.4% strongly agree and 19.6% agree that the resulting NPC has a large variety of actions. The results of the questionnaire also found that 69.6% were very unrealistic and 30.4% said that NPCs were unrealistic or did not imitate human behavior.

References

[1] Ge, M., Hu, J., Liu, M., & Zhang, Y. 2018. Reassembly

classification selection method based on the Markov Chain.

Assembly Automation, 38(4), 476–486.

https://doi.org/10.1108/AA-03-2017-043.

[2] Hassan, I., Faisal, M., & Arif, Y. M. 2018. Implementation

of Artificial Bee Colony Algorithm to Generate NPC

Behavior in Survival Horror Game " Left Alone " As A

Media Introduction to House of Cut Nyak Dhien. 7(1), 16–

24.

[3] Hernawan, S. R. 2018. Penerapan Metode Finite State

Machine Pada Game “ The Mahasiswa ” Guna Membangun

Perilaku Non Playable Character Halaman Pengesahan

Dosen Penguji Penerapan Metode Finite State Machine Pada

Game “ The Mahasiswa ” Guna Membangun Perilaku Non

Playable Char. Jurnal Edukasi Dan Penelitian Informatika

(JEPIN), Retrieved from https://jurnal.untan.ac.id/.

[4] Hidayat, E. W., Rachman, A. N., & Azim, M. F. 2019.

Penerapan Finite State Machine pada Battle Game Berbasis

Augmented Reality. Jurnal Edukasi Dan Penelitian

Informatika (JEPIN), 5(1), 54.

https://doi.org/10.26418/jp.v5i1.29848

[5] Kasse, I., Didiharyono, D., & Maulidina, M. 2020. Metode

Markov Chain untuk Menghitung Premi Asuransi pada

Pasien Penderita Penyakit Demam Berdarah Dengue. AlKhwarizmi: Jurnal Pendidikan Matematika Dan Ilmu

Pengetahuan Alam, 7(2), 151–160.

https://doi.org/10.24256/jpmipa.v7i2.1251

[6] Kopel, M., & Hajas, T. 2018. Implementing AI for Nonplayer Characters in 3D Video Games. Lecture Notes in

Computer Science (Including Subseries Lecture Notes in

Artificial Intelligence and Lecture Notes in Bioinformatics),

10751 LNAI, 610–619. https://doi.org/10.1007/978-3-319-

75417-8_57

[7] Sri, U. , Wayan, M. , Purnaba, I. G. P. (2018). Evaluasi

Numerik Penduga Fungsi Nilai Harapan Dan Fungsi Ragam

Proses Poisson Majemuk Dengan Intensitas Eksponensial

Fungsi Linear. Journal of Mathematics and Its Applications,

17(2), 157-169. https://doi.org/10.29244/jmap.17.2.157-169.

[8] Saprudin, S., Liliasari, L., Setiawan, A., & Prihatmanto, A.

S. 2019. The effectiveness of using digital game towards

students’ academic achievement in small and large classes: A

comparative research. International Journal of Learning,

Teaching and Educational Research, 18(12), 196–210.

https://doi.org/10.26803/ijlter.18.12.12

[9] Tadayon, M., & Pottie, G. J. 2020. Predicting Student

Performance in an Educational Game Using a Hidden

Markov Model. IEEE Transactions on Education, 63(4),

299–304. https://doi.org/10.1109/TE.2020.2984900

[10] Tirtana, A., & Pumpungan, M. (2020). Perancangan Game

Visual Novel “ Coconut Kids ” Sebagai Sarana Edukasi

Pelestarian Alam. Universitas 17 Agustus 1945 Surabaya.

Retrieved from http://repository.untagsby.ac.id/id/eprint/5224.

[11] Wang, L., Huang, W., Li, Y., Evans, J., & He, S. (2020).

Multi-AI competing and winning against humans in iterated

rock-paper-scissors game. Sci Rep 10, 13873 (2020).

https://doi.org/10.1038/s41598-020-70544-7.

[12] Yulsilviana, E., & Ekawati, H. 2019. Penerapan Metode

Finite State Machine (Fsm) Pada Game Agent Legenda Anak

Borneo. Sebatik, 23(1), 116–123.

https://doi.org/10.46984/sebatik.v23i1.453

[13] Zhou, Y., Wang, L., Zhong, R., & Tan, Y. 2018. A Markov

Chain Based Demand Prediction Model for Stations in Bike

Sharing Systems. Mathematical Problems in Engineering,

2018. https://doi.org/10.1155/2018/8028714

[14] Zhu, X. 2019. Behavior tree design of intelligent behavior of

non-player character (NPC) based on Unity3D. Journal of

Intelligent and Fuzzy Systems, 37(5), 6071–6079.

https://doi.org/10.3233/JIFS-179190

[15] Zou, Q., Li, Q., Guo, H., & Shi, J. 2018. A discrete-time and

finite-state Markov Chain model for association football

matches. Communications in Statistics: Simulation and

Computation, 47(8), 2476–2485.

https://doi.org/10.1080/03610918.2017.1348518

Downloads

Published

2021-10-13

Issue

Section

Articles