Teknologi Artificial Intelligence (AI) Vision Swift dalam Sistem Pemantauan Latihan Bulu Tangkis dengan Algoritma Optical Flow

Mora Hakim Siregar; Dadang Iskandar Mulyana

doi:10.35870/jimik.v5i3.1027

Vol. 5 No. 3 (2024) • Articles

Open Access

Teknologi Artificial Intelligence (AI) Vision Swift dalam Sistem Pemantauan Latihan Bulu Tangkis dengan Algoritma Optical Flow

Mora Hakim Siregar

Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Dadang Iskandar Mulyana

Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

DOI: https://doi.org/10.35870/jimik.v5i3.1027

Published: September 20, 2024 Pages: 3349-3361

Original Full-Text Article

Download published version for reading and archiving

Download PDF

Abstract

Badminton is one of the most popular sports in Indonesia. In fact, Indonesia often wins various badminton competitions at the international level. Many people enjoy playing badminton, but many of them do not know whether their shots are good or whether they understand the basic techniques of badminton correctly. Additionally, many of them want to improve their skills but do not have enough time to train with a professional coach. This research aims to develop a badminton training monitoring system based on AI Vision technology using the Swift programming language. This system is expected to help badminton players evaluate and improve the quality of their shots independently. The main focus of this research is to optimize computational accuracy by using the Optical Flow algorithm to track the movement of the shuttlecock during training. In developing this system, the Optical Flow algorithm is used to analyze the shuttlecock's trajectory and its drop points. The results of this research show that testing 20 shots using shuttlecock trajectory can be accurately detected by the system with an accuracy of 97.22%. Meanwhile, the system's accuracy in tracking the placement of the shuttlecock in the opponent's service area is 94.50%.

Keywords:

Optical Flow Badminton Vision Shuttlecock

Article Metrics & Downloads Graph

Monthly Download Trends:

Author Biographies

Mora Hakim Siregar Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Program Studi Teknik Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

Dadang Iskandar Mulyana Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Program Studi Teknik Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

How to Cite

Siregar, M. H., & Mulyana, D. I. (2024). Teknologi Artificial Intelligence (AI) Vision Swift dalam Sistem Pemantauan Latihan Bulu Tangkis dengan Algoritma Optical Flow. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(3), 3349-3361. https://doi.org/10.35870/jimik.v5i3.1027

License

Creative Commons Attribution 4.0 International License (CC BY 4.0)

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License .

Share: You are free to copy, distribute, and transmit the work in any medium or format.
Adapt: You are free to remix, transform, and build upon the work for any purpose, even commercially.
Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

Copyright & Retention: Authors retain copyright without restrictions and grant this journal the right of first publication under an open-access model. The journal retains non-exclusive publishing rights for archiving, indexing, and scholarly dissemination.

References

Total: 26 References

Adrian, R. (2017). Deep learning for computer vision with python.
Aiyub, F. F., & Munawir, M. (2019). Kontrol mouse menggunakan webcam berdasarkan deteksi warna. JTIM: Jurnal Teknologi Informasi dan Multimedia, 1(1), 73-77.
Aslani, S., & Mahdavi-Nasab, H. (2013). Optical flow based moving object detection and tracking for traffic surveillance. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 7(9), 1252-1256. DOI: https://doi.org/10.31004/jpdk.v4i4.5819.
Choi, H., Kang, B., & Kim, D. (2022). Moving object tracking based on sparse optical flow with moving window and target estimator. Sensors, 22(8), 2878.
Dewantara, B. S. B., Pramadihanto, D., Sesulihatien, W. T., Sudarsono, A., Oktavianto, H., Sumantri, B., & Winarno, I. (2022). Pembuatan Sistem Mikroskop Digital Terintegrasi dengan Pengolahan Citra untuk Pengembangan Perangkat Pembelajaran IPA di SMPIT Al Uswah Surabaya. J-Dinamika: Jurnal Pengabdian Masyarakat, 7(2), 173-180. DOI: 10.25047/j-dinamika.v7i2.2413.
Dewantara, B. S. B., Pramadihanto, D., Sesulihatien, W. T., Sudarsono, A., Oktavianto, H., Sumantri, B., & Winarno, I. (2022). Pembuatan Sistem Mikroskop Digital Terintegrasi dengan Pengolahan Citra untuk Pengembangan Perangkat Pembelajaran IPA di SMPIT Al Uswah Surabaya. J-Dinamika: Jurnal Pengabdian Masyarakat, 7(2), 173-180. DOI: 10.25047/j-dinamika.v7i2.2413.
Fadli, R., Syaputra, H., Mirza, H., & Oktaviani, N. (2022). Perancangan Artificial Intelegence Hand Tracking menggunakan Algoritma Pyramidal Lucas-Kanade Optical Flow. Jurnal Pendidikan dan Konseling (JPDK), 4(4), 2445-2451.
Handika, R. N., & Rahmani, B. (2023). Deteksi Arah Gerak Bola dengan Metode Optical Flow pada Robot Penjaga Gawang. Progresif: Jurnal Ilmiah Komputer, 19(2), 623-635. DOI: 10.35889/progresif.v19i2.1455.
Hermawan, H., Saputra, D. I. S., & Hariawan, A. (2022). Markerless Augmented Reality Motorcycle Engine Using Database for Interactive Online Learning Media. International Journal of Mechanical Engineering, 7(1), 515-523.
Hermawansa, H., & Kalsum, T. U. (2019). Analisis kinerja sensor pada robot pendeteksi kotoran debu dan air. ILKOM Jurnal Ilmiah, 11(1), 53-58. DOI: https://doi.org/10.33096/ilkom.v11i1.405.53-58.
Hudah, A., Huda, N., & Fauzi, A. R. (2019). LONGSOR MERATA (TEKNOLOGI SENSOR KAMERA SEBAGAI KACAMATA PENGENAL TEKS UNTUK TUNA NETRA). Jurnal Teknologi dan Manajemen Informatika, 5(1).
Indriyani, I., & Hasibuan, H. N. S. (2022). Pengelolaan Optical Character Recognition For Invent And Outcome Management At The Batang Toru Camat Office, Tapanuli Selatan. Journal of Computer Science and Informatics Engineering, 146-152. DOI: https://doi.org/10.55537/cosie.v1i3.74.
Kaloh, K. M., Poekoel, V. C., & Putro, M. D. (2018). Perbandingan Algoritma Background Subtraction dan Optical Flow Untuk Deteksi Manusia. Jurnal Teknik Informatika, 13(1). DOI: https://doi.org/10.35793/jti.v13i1.20186.
Lakshmanan, V., Görner, M., & Gillard, R. (2021). Practical machine learning for computer vision. " O'Reilly Media, Inc.".
Lestari, I. N. T., & Mulyana, D. I. (2022). Implementation of OCR (Optical Character Recognition) Using Tesseract in Detecting Character in Quotes Text Images. Journal of Applied Engineering and Technological Science (JAETS), 4(1), 58-63. DOI: https://doi.org/10.37385/jaets.v4i1.905.
Makahaube, S., Sambul, A. M., & Sompie, S. R. (2021). Implementation of Gesture Recognition Technology for Self-Education Service Platform. Jurnal Teknik Informatika, 16(4), 465-472. DOI: https://doi.org/10.35793/jti.v16i4.34210.
Malau, F. R., & Mulyana, D. I. (2022). Classification of Edelweiss Flowers Using Data Augmentation and Linear Discriminant Analysis Methods. Journal of Applied Engineering and Technological Science (JAETS), 4(1), 139-148. DOI: https://doi.org/10.37385/jaets.v4i1.960.
Mendes, P. A., Mendes, M., Coimbra, A. P., & Crisóstomo, M. M. (2019, July). Movement detection and moving object distinction based on optical flow. In Proceedings of the Lecture Notes in Engineering and Computer Science: Proceedings of The World Congress on Engineering, London, UK (pp. 3-5).
Mulyana, D. I., Saepudin, A., & Yel, M. B. (2022). Health Detection of Betal Leaves Using Self-Organizing Map and Thresholding Algorithm. Journal of Applied Engineering and Technological Science (JAETS), 4(1), 180-189. DOI: https://doi.org/10.37385/jaets.v4i1.957.
Suratna, A. (2021). Mengenal Definisi, Fungsi, dan Simbol dalam Flowmap. agussuratna. net.
Syaharuddin, A. Z., Endang, A. H., & Alfarizi, D. A. (2023). Rancang Bangun Sistem Monitoring Aktivitas Pelanggan berbasis Citra Menggunakan Optical Flow. Journal of Embedded Systems, Security and Intelligent Systems, 4(2), 126-131.
Theodorou, C., Velisavljevic, V., & Dyo, V. (2022). Visual SLAM for dynamic environments based on object detection and optical flow for dynamic object removal. Sensors, 22(19), 7553.
Umar, U., Soelistijorini, R., & Darwito, H. A. (2011). Tracking arah gerakan telunjuk jari berbasis webcam menggunakan Metode Optical Flow.
Wiraatmaja, N. K. A., & Utaminingrum, F. (2021). Implementasi Metode Optical Flow untuk Pemilihan Menu Display pada Rancang Bangun Sistem Deteksi Pergerakan Kepala. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 5(9), 3966-3972.
Yang, Y., Loquercio, A., Scaramuzza, D., & Soatto, S. (2019). Unsupervised moving object detection via contextual information separation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 879-888).
Yu, H., Tang, Y., Xie, E., Mao, J., Yuan, J., Luo, P., & Nie, Z. (2023). Vehicle-infrastructure cooperative 3d object detection via feature flow prediction. arXiv preprint arXiv:2303.10552. DOI: https://doi.org/10.48550/arXiv.2303.10552.