Perancangan Sistem Pendukung Objek Deteksi untuk Permainan Kartu Cardfight!Vanguard Menggunakan Aplikasi Roboflow dan Flask

Authors

  • Cornelius Arvel Pratama Tungady Universitas Kristen Satya Wacana
  • Hindriyanto Dwi Purnomo Universitas Kristen Satya Wacana

Keywords:

TCG, Remote play, Vanguard, Roboflow, Flask

Abstract

The covid-19 pandemic that emerged in early 2020 has affected our activities with various limitations, for this reason the government has implemented several protocols starting from using masks, maintaining cleanliness, and social distancing. It is also undeniable that humans are social creatures, in this context boredom is the main enemy. There are some activities to be able relieve stress such as reading, listening to music, watching, or playing a game. TCG (Trading Card Game) is an artificial game that build in with such various interesting themes. Card games are generally played with other people, but what would happen if the card game was played in the covid-19 pandemic. Of course, the biggest obstacle we will be facing are distance and time. Konami as a card game manufacturer and developer has a brilliant idea by implementing Remote Duel where tournaments and other official matches can be held virtually. Cardfight!Vanguard is no exception, which has a gameplay that really depends on the interaction between players. Remote play requires players to use a camera that will take pictures of the card field while playing. This study uses the Waterfall Model which will be taken from dataset preparation, train the data, perform the Annotate process, and carry out a web-based implementation using the flask framework so that it can be used and tested for its functionality using one of tensorflow's product technologies, Roboflow, which is an application that specifically designed to be able to assist in the process of creating and recognizing objects. The results obtained by using flask as a web base can be seen to perform card object recognition properly so that it can display data from the detected cards.

References

Jahangir, M. A., Muheem, A., & Rizvi, M. F. (2020). Coronavirus (COVID-19): history, current knowledge and pipeline medications. Journal of Pharmaceutical Research Science & Technology [ISSN: 2583-3332], 4(1), 1-9.

Afini, M., & Hanifah, H. (2021). Stresor dan Penanggulangan Stres Selama Masa Awal Pandemi Covid-19. Psikostudia: Jurnal Psikologi, 10(3), 294-305.

Bicheno, T. (2022). The Impact of the COVID-19 Pandemic on the International Marketing Strategies of MNE’s: A Report on the Konami Company and ‘YuGiOh!’. Essex Student Journal, 5-6.

Hurst, W., Withington, A., & Kolivand, H. (2022). Virtual conference design: features and obstacles. Multimedia Tools and Applications, 16908-16909.

Ditrih, H., Grgić, S., & Turković, L. (2021). Real-Time Detection and Recognition of Cards in the Game of Set. International Symposium on Electronics in Marine.

Grinberg, M. (2018). Flask Web Development: Developing Web Applications with Python. United States of America: O'Reilly Media, Inc.

Ciaglia, F., Zuppichini, F. S., Guerrie, P., McQuade, M., & Solawetz, J. (2022). Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark. arXiv preprint arXiv:2211.13523.

Zhang, P., Zhong, Y., & Li, X. (2019). SlimYOLOv3: Narrower, faster and better for real-time UAV applications. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (pp. 0-0).

Bahaghighat, M., Akbari, L., & Xin, Q. (2019). A machine learning-based approach for counting blister cards within drug packages. IEEE Access, 7, 83785-83796.

Prasetia, D. D., Yuswanto, A., & Wibowo, B. (2022). Design of Machine Learning Detection Mask Using Yolo and Darknet on Nvidia Jetson Nano. Teknokom, 5(1), 88-95.

Xia, Y., Nguyen, M., & Yan, W. Q. (2023, February). A Real-Time Kiwifruit Detection Based on Improved YOLOv7. In Image and Vision Computing: 37th International Conference, IVCNZ 2022, Auckland, New Zealand, November 24–25, 2022, Revised Selected Papers (pp. 48-61). Cham: Springer Nature Switzerland.

Khoo, E. J., & Lantos, J. D. (2020). Lessons learned from the COVIDâ€19 pandemic. Acta Paediatrica (Oslo, Norway: 1992), 109(7), 1323.

Ramadhany, A., Firdausi, A. Z., & Karyani, U. (2021). Stres pada mahasiswa selama pandemi covid-19. Jurnal Psikologi Insight, 5(2), 65-71.

Raschka, S., Patterson, J., & Nolet, C. (2020). Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information, 11(4), 193.

Published

2023-07-30

How to Cite

Tungady, C. A. P., & Purnomo, H. D. (2023). Perancangan Sistem Pendukung Objek Deteksi untuk Permainan Kartu Cardfight!Vanguard Menggunakan Aplikasi Roboflow dan Flask. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 6(3), 283–290. Retrieved from https://openjournal.unpam.ac.id/index.php/JTSI/article/view/30303