Teknologi Artificial Intelligence (AI) Vision Swift dalam Sistem Pemantauan Latihan Bulu Tangkis dengan Algoritma Optical Flow
Article Sidebar
📅
Published:
Sep 20, 2024
Main Article Content
Abstract
Article Summary
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
Article Keywords
Downloads
Download data is not yet available.
Article Details
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
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-BY 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
License: All articles are licensed under 
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.
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.