Pengaplikasian sport analytic pada klasifikasi posisi defense permainan bulu tangkis dengan convolutional neural network = Application of sports analytics in defensive positions classification in badminton using convolutional neural network

Sangkang, Natanael (2024) Pengaplikasian sport analytic pada klasifikasi posisi defense permainan bulu tangkis dengan convolutional neural network = Application of sports analytics in defensive positions classification in badminton using convolutional neural network. Bachelor thesis, Universitas Pelita Harapan.

	Text (Title) Title.pdf Restricted to Registered users only Available under License Creative Commons Attribution Non-commercial Share Alike. Download (73kB)
	Text (Abstract) Abstract.pdf Restricted to Registered users only Available under License Creative Commons Attribution Non-commercial Share Alike. Download (206kB)
	Text (ToC) ToC.pdf Restricted to Registered users only Available under License Creative Commons Attribution Non-commercial Share Alike. Download (489kB)
	Text (Chapter1) Chapter1.pdf Restricted to Registered users only Available under License Creative Commons Attribution Non-commercial Share Alike. Download (545kB)
	Text (Chapter2) Chapter2.pdf Restricted to Registered users only Available under License Creative Commons Attribution Non-commercial Share Alike. Download (1MB)
	Text (Chapter3) Chapter3.pdf Restricted to Registered users only Available under License Creative Commons Attribution Non-commercial Share Alike. Download (833kB)
	Text (Chapter4) Chapter4.pdf Restricted to Registered users only Available under License Creative Commons Attribution Non-commercial Share Alike. Download (1MB)
	Text (Chapter5) Chapter5.pdf Restricted to Registered users only Available under License Creative Commons Attribution Non-commercial Share Alike. Download (204kB)
	Text (Bibliography) Bibliography.pdf Restricted to Registered users only Available under License Creative Commons Attribution Non-commercial Share Alike. Download (178kB)
	Text (Appendices) Appendices.pdf Restricted to Repository staff only Available under License Creative Commons Attribution Non-commercial Share Alike. Download (2MB)

Abstract

Sport analytic merupakan penerapan data dan statistik untuk memahami dan meningkatkan kinerja atlet, tim, maupun strategi dalam olahraga. Penggunaan sport analytic telah mengalami perkembangan signifikan, terutama dengan semakin mudahnya pengumpulan data dan kemajuan teknologi dalam pemrosesan data.Sport analytic juga diterapkan dalam olahraga bulutangkis untuk meningkatkan strategi permainan dan kinerja atlet. Data yang dikumpulkan dari kompetisi bulutangkis dapat memberikan wawasan mendalam mengenai pola permainan dan kekuatan lawan. Penerapan Convolutional Neural Network (CNN) dalam olahraga telah mulai menunjukkan hasil yang menjanjikan, terutama dalam analisis posisi, gerakan, dan strategi permainan. Dengan menggunakan data video, CNN dapat mengidentifikasi data.Implementasi sistem dalam penelitian ini dibagi tiga bagian yaitu : pertama, melakukan pengumpulan data dengan motion capture dari video pertandingan atlet. Kedua, data yang telah terkumpul akan dikelompokan menggunakan perangkat lunak orange3 untuk membaca data yang sama dan dikelompokkan lalu ditampilkan dalam bentuk histogram dan distance map. Ketiga, melakukan prediksi terhadap data yang telah dikelompokan. Prediksi menggunakan algoritma tree sebagai parameter dan klasifikasi dalam memproses data. / Sports analytics involves the application of data and statistics to understand and enhance the performance of athletes, teams, and strategies in sports. The use of sports analytics has seen significant development, particularly with the increasing ease of data collection and advancements in data processing technology. Sports analytics is also applied in badminton to improve game strategies and athlete performance. Data collected from badminton competitions can provide deep insights into game patterns and opponent strengths. The implementation of Convolutional Neural Networks (CNNs) in sports has begun to show promising results, especially in analyzing positions, movements, and game strategies. By utilizing video data, CNNs can effectively identify patterns.The implementation of the system in this research is divided into three parts: firstly, collecting data using motion capture from athlete competition videos. Secondly, the collected data will be processed using Orange3 software to read and categorize the data, which will then be displayed in the form of histograms and distance maps. Thirdly, making predictions based on the categorized data. Predictions utilize tree algorithms as parameters and classifications for data processing.

Actions (login required)

View Item